Upload 199 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+figure/multitask.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so.  This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software.  The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable.  Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
+computer or modifying a private copy.  Propagation includes copying,
+distribution (with or without modification), making available to the
+public, and in some countries other activities as well.
+
+  To "convey" a work means any kind of propagation that enables other
+parties to make or receive copies.  Mere interaction with a user through
+a computer network, with no transfer of a copy, is not conveying.
+
+  An interactive user interface displays "Appropriate Legal Notices"
+to the extent that it includes a convenient and prominently visible
+feature that (1) displays an appropriate copyright notice, and (2)
+tells the user that there is no warranty for the work (except to the
+extent that warranties are provided), that licensees may convey the
+work under this License, and how to view a copy of this License.  If
+the interface presents a list of user commands or options, such as a
+menu, a prominent item in the list meets this criterion.
+
+  1. Source Code.
+
+  The "source code" for a work means the preferred form of the work
+for making modifications to it.  "Object code" means any non-source
+form of a work.
+
+  A "Standard Interface" means an interface that either is an official
+standard defined by a recognized standards body, or, in the case of
+interfaces specified for a particular programming language, one that
+is widely used among developers working in that language.
+
+  The "System Libraries" of an executable work include anything, other
+than the work as a whole, that (a) is included in the normal form of
+packaging a Major Component, but which is not part of that Major
+Component, and (b) serves only to enable use of the work with that
+Major Component, or to implement a Standard Interface for which an
+implementation is available to the public in source code form.  A
+"Major Component", in this context, means a major essential component
+(kernel, window system, and so on) of the specific operating system
+(if any) on which the executable work runs, or a compiler used to
+produce the work, or an object code interpreter used to run it.
+
+  The "Corresponding Source" for a work in object code form means all
+the source code needed to generate, install, and (for an executable
+work) run the object code and to modify the work, including scripts to
+control those activities.  However, it does not include the work's
+System Libraries, or general-purpose tools or generally available free
+programs which are used unmodified in performing those activities but
+which are not part of the work.  For example, Corresponding Source
+includes interface definition files associated with source files for
+the work, and the source code for shared libraries and dynamically
+linked subprograms that the work is specifically designed to require,
+such as by intimate data communication or control flow between those
+subprograms and other parts of the work.
+
+  The Corresponding Source need not include anything that users
+can regenerate automatically from other parts of the Corresponding
+Source.
+
+  The Corresponding Source for a work in source code form is that
+same work.
+
+  2. Basic Permissions.
+
+  All rights granted under this License are granted for the term of
+copyright on the Program, and are irrevocable provided the stated
+conditions are met.  This License explicitly affirms your unlimited
+permission to run the unmodified Program.  The output from running a
+covered work is covered by this License only if the output, given its
+content, constitutes a covered work.  This License acknowledges your
+rights of fair use or other equivalent, as provided by copyright law.
+
+  You may make, run and propagate covered works that you do not
+convey, without conditions so long as your license otherwise remains
+in force.  You may convey covered works to others for the sole purpose
+of having them make modifications exclusively for you, or provide you
+with facilities for running those works, provided that you comply with
+the terms of this License in conveying all material for which you do
+not control copyright.  Those thus making or running the covered works
+for you must do so exclusively on your behalf, under your direction
+and control, on terms that prohibit them from making any copies of
+your copyrighted material outside their relationship with you.
+
+  Conveying under any other circumstances is permitted solely under
+the conditions stated below.  Sublicensing is not allowed; section 10
+makes it unnecessary.
+
+  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
+
+  No covered work shall be deemed part of an effective technological
+measure under any applicable law fulfilling obligations under article
+11 of the WIPO copyright treaty adopted on 20 December 1996, or
+similar laws prohibiting or restricting circumvention of such
+measures.
+
+  When you convey a covered work, you waive any legal power to forbid
+circumvention of technological measures to the extent such circumvention
+is effected by exercising rights under this License with respect to
+the covered work, and you disclaim any intention to limit operation or
+modification of the work as a means of enforcing, against the work's
+users, your or third parties' legal rights to forbid circumvention of
+technological measures.
+
+  4. Conveying Verbatim Copies.
+
+  You may convey verbatim copies of the Program's source code as you
+receive it, in any medium, provided that you conspicuously and
+appropriately publish on each copy an appropriate copyright notice;
+keep intact all notices stating that this License and any
+non-permissive terms added in accord with section 7 apply to the code;
+keep intact all notices of the absence of any warranty; and give all
+recipients a copy of this License along with the Program.
+
+  You may charge any price or no price for each copy that you convey,
+and you may offer support or warranty protection for a fee.
+
+  5. Conveying Modified Source Versions.
+
+  You may convey a work based on the Program, or the modifications to
+produce it from the Program, in the form of source code under the
+terms of section 4, provided that you also meet all of these conditions:
+
+    a) The work must carry prominent notices stating that you modified
+    it, and giving a relevant date.
+
+    b) The work must carry prominent notices stating that it is
+    released under this License and any conditions added under section
+    7.  This requirement modifies the requirement in section 4 to
+    "keep intact all notices".
+
+    c) You must license the entire work, as a whole, under this
+    License to anyone who comes into possession of a copy.  This
+    License will therefore apply, along with any applicable section 7
+    additional terms, to the whole of the work, and all its parts,
+    regardless of how they are packaged.  This License gives no
+    permission to license the work in any other way, but it does not
+    invalidate such permission if you have separately received it.
+
+    d) If the work has interactive user interfaces, each must display
+    Appropriate Legal Notices; however, if the Program has interactive
+    interfaces that do not display Appropriate Legal Notices, your
+    work need not make them do so.
+
+  A compilation of a covered work with other separate and independent
+works, which are not by their nature extensions of the covered work,
+and which are not combined with it such as to form a larger program,
+in or on a volume of a storage or distribution medium, is called an
+"aggregate" if the compilation and its resulting copyright are not
+used to limit the access or legal rights of the compilation's users
+beyond what the individual works permit.  Inclusion of a covered work
+in an aggregate does not cause this License to apply to the other
+parts of the aggregate.
+
+  6. Conveying Non-Source Forms.
+
+  You may convey a covered work in object code form under the terms
+of sections 4 and 5, provided that you also convey the
+machine-readable Corresponding Source under the terms of this License,
+in one of these ways:
+
+    a) Convey the object code in, or embodied in, a physical product
+    (including a physical distribution medium), accompanied by the
+    Corresponding Source fixed on a durable physical medium
+    customarily used for software interchange.
+
+    b) Convey the object code in, or embodied in, a physical product
+    (including a physical distribution medium), accompanied by a
+    written offer, valid for at least three years and valid for as
+    long as you offer spare parts or customer support for that product
+    model, to give anyone who possesses the object code either (1) a
+    copy of the Corresponding Source for all the software in the
+    product that is covered by this License, on a durable physical
+    medium customarily used for software interchange, for a price no
+    more than your reasonable cost of physically performing this
+    conveying of source, or (2) access to copy the
+    Corresponding Source from a network server at no charge.
+
+    c) Convey individual copies of the object code with a copy of the
+    written offer to provide the Corresponding Source.  This
+    alternative is allowed only occasionally and noncommercially, and
+    only if you received the object code with such an offer, in accord
+    with subsection 6b.
+
+    d) Convey the object code by offering access from a designated
+    place (gratis or for a charge), and offer equivalent access to the
+    Corresponding Source in the same way through the same place at no
+    further charge.  You need not require recipients to copy the
+    Corresponding Source along with the object code.  If the place to
+    copy the object code is a network server, the Corresponding Source
+    may be on a different server (operated by you or a third party)
+    that supports equivalent copying facilities, provided you maintain
+    clear directions next to the object code saying where to find the
+    Corresponding Source.  Regardless of what server hosts the
+    Corresponding Source, you remain obligated to ensure that it is
+    available for as long as needed to satisfy these requirements.
+
+    e) Convey the object code using peer-to-peer transmission, provided
+    you inform other peers where the object code and Corresponding
+    Source of the work are being offered to the general public at no
+    charge under subsection 6d.
+
+  A separable portion of the object code, whose source code is excluded
+from the Corresponding Source as a System Library, need not be
+included in conveying the object code work.
+
+  A "User Product" is either (1) a "consumer product", which means any
+tangible personal property which is normally used for personal, family,
+or household purposes, or (2) anything designed or sold for incorporation
+into a dwelling.  In determining whether a product is a consumer product,
+doubtful cases shall be resolved in favor of coverage.  For a particular
+product received by a particular user, "normally used" refers to a
+typical or common use of that class of product, regardless of the status
+of the particular user or of the way in which the particular user
+actually uses, or expects or is expected to use, the product.  A product
+is a consumer product regardless of whether the product has substantial
+commercial, industrial or non-consumer uses, unless such uses represent
+the only significant mode of use of the product.
+
+  "Installation Information" for a User Product means any methods,
+procedures, authorization keys, or other information required to install
+and execute modified versions of a covered work in that User Product from
+a modified version of its Corresponding Source.  The information must
+suffice to ensure that the continued functioning of the modified object
+code is in no case prevented or interfered with solely because
+modification has been made.
+
+  If you convey an object code work under this section in, or with, or
+specifically for use in, a User Product, and the conveying occurs as
+part of a transaction in which the right of possession and use of the
+User Product is transferred to the recipient in perpetuity or for a
+fixed term (regardless of how the transaction is characterized), the
+Corresponding Source conveyed under this section must be accompanied
+by the Installation Information.  But this requirement does not apply
+if neither you nor any third party retains the ability to install
+modified object code on the User Product (for example, the work has
+been installed in ROM).
+
+  The requirement to provide Installation Information does not include a
+requirement to continue to provide support service, warranty, or updates
+for a work that has been modified or installed by the recipient, or for
+the User Product in which it has been modified or installed.  Access to a
+network may be denied when the modification itself materially and
+adversely affects the operation of the network or violates the rules and
+protocols for communication across the network.
+
+  Corresponding Source conveyed, and Installation Information provided,
+in accord with this section must be in a format that is publicly
+documented (and with an implementation available to the public in
+source code form), and must require no special password or key for
+unpacking, reading or copying.
+
+  7. Additional Terms.
+
+  "Additional permissions" are terms that supplement the terms of this
+License by making exceptions from one or more of its conditions.
+Additional permissions that are applicable to the entire Program shall
+be treated as though they were included in this License, to the extent
+that they are valid under applicable law.  If additional permissions
+apply only to part of the Program, that part may be used separately
+under those permissions, but the entire Program remains governed by
+this License without regard to the additional permissions.
+
+  When you convey a copy of a covered work, you may at your option
+remove any additional permissions from that copy, or from any part of
+it.  (Additional permissions may be written to require their own
+removal in certain cases when you modify the work.)  You may place
+additional permissions on material, added by you to a covered work,
+for which you have or can give appropriate copyright permission.
+
+  Notwithstanding any other provision of this License, for material you
+add to a covered work, you may (if authorized by the copyright holders of
+that material) supplement the terms of this License with terms:
+
+    a) Disclaiming warranty or limiting liability differently from the
+    terms of sections 15 and 16 of this License; or
+
+    b) Requiring preservation of specified reasonable legal notices or
+    author attributions in that material or in the Appropriate Legal
+    Notices displayed by works containing it; or
+
+    c) Prohibiting misrepresentation of the origin of that material, or
+    requiring that modified versions of such material be marked in
+    reasonable ways as different from the original version; or
+
+    d) Limiting the use for publicity purposes of names of licensors or
+    authors of the material; or
+
+    e) Declining to grant rights under trademark law for use of some
+    trade names, trademarks, or service marks; or
+
+    f) Requiring indemnification of licensors and authors of that
+    material by anyone who conveys the material (or modified versions of
+    it) with contractual assumptions of liability to the recipient, for
+    any liability that these contractual assumptions directly impose on
+    those licensors and authors.
+
+  All other non-permissive additional terms are considered "further
+restrictions" within the meaning of section 10.  If the Program as you
+received it, or any part of it, contains a notice stating that it is
+governed by this License along with a term that is a further
+restriction, you may remove that term.  If a license document contains
+a further restriction but permits relicensing or conveying under this
+License, you may add to a covered work material governed by the terms
+of that license document, provided that the further restriction does
+not survive such relicensing or conveying.
+
+  If you add terms to a covered work in accord with this section, you
+must place, in the relevant source files, a statement of the
+additional terms that apply to those files, or a notice indicating
+where to find the applicable terms.
+
+  Additional terms, permissive or non-permissive, may be stated in the
+form of a separately written license, or stated as exceptions;
+the above requirements apply either way.
+
+  8. Termination.
+
+  You may not propagate or modify a covered work except as expressly
+provided under this License.  Any attempt otherwise to propagate or
+modify it is void, and will automatically terminate your rights under
+this License (including any patent licenses granted under the third
+paragraph of section 11).
+
+  However, if you cease all violation of this License, then your
+license from a particular copyright holder is reinstated (a)
+provisionally, unless and until the copyright holder explicitly and
+finally terminates your license, and (b) permanently, if the copyright
+holder fails to notify you of the violation by some reasonable means
+prior to 60 days after the cessation.
+
+  Moreover, your license from a particular copyright holder is
+reinstated permanently if the copyright holder notifies you of the
+violation by some reasonable means, this is the first time you have
+received notice of violation of this License (for any work) from that
+copyright holder, and you cure the violation prior to 30 days after
+your receipt of the notice.
+
+  Termination of your rights under this section does not terminate the
+licenses of parties who have received copies or rights from you under
+this License.  If your rights have been terminated and not permanently
+reinstated, you do not qualify to receive new licenses for the same
+material under section 10.
+
+  9. Acceptance Not Required for Having Copies.
+
+  You are not required to accept this License in order to receive or
+run a copy of the Program.  Ancillary propagation of a covered work
+occurring solely as a consequence of using peer-to-peer transmission
+to receive a copy likewise does not require acceptance.  However,
+nothing other than this License grants you permission to propagate or
+modify any covered work.  These actions infringe copyright if you do
+not accept this License.  Therefore, by modifying or propagating a
+covered work, you indicate your acceptance of this License to do so.
+
+  10. Automatic Licensing of Downstream Recipients.
+
+  Each time you convey a covered work, the recipient automatically
+receives a license from the original licensors, to run, modify and
+propagate that work, subject to this License.  You are not responsible
+for enforcing compliance by third parties with this License.
+
+  An "entity transaction" is a transaction transferring control of an
+organization, or substantially all assets of one, or subdividing an
+organization, or merging organizations.  If propagation of a covered
+work results from an entity transaction, each party to that
+transaction who receives a copy of the work also receives whatever
+licenses to the work the party's predecessor in interest had or could
+give under the previous paragraph, plus a right to possession of the
+Corresponding Source of the work from the predecessor in interest, if
+the predecessor has it or can get it with reasonable efforts.
+
+  You may not impose any further restrictions on the exercise of the
+rights granted or affirmed under this License.  For example, you may
+not impose a license fee, royalty, or other charge for exercise of
+rights granted under this License, and you may not initiate litigation
+(including a cross-claim or counterclaim in a lawsuit) alleging that
+any patent claim is infringed by making, using, selling, offering for
+sale, or importing the Program or any portion of it.
+
+  11. Patents.
+
+  A "contributor" is a copyright holder who authorizes use under this
+License of the Program or a work on which the Program is based.  The
+work thus licensed is called the contributor's "contributor version".
+
+  A contributor's "essential patent claims" are all patent claims
+owned or controlled by the contributor, whether already acquired or
+hereafter acquired, that would be infringed by some manner, permitted
+by this License, of making, using, or selling its contributor version,
+but do not include claims that would be infringed only as a
+consequence of further modification of the contributor version.  For
+purposes of this definition, "control" includes the right to grant
+patent sublicenses in a manner consistent with the requirements of
+this License.
+
+  Each contributor grants you a non-exclusive, worldwide, royalty-free
+patent license under the contributor's essential patent claims, to
+make, use, sell, offer for sale, import and otherwise run, modify and
+propagate the contents of its contributor version.
+
+  In the following three paragraphs, a "patent license" is any express
+agreement or commitment, however denominated, not to enforce a patent
+(such as an express permission to practice a patent or covenant not to
+sue for patent infringement).  To "grant" such a patent license to a
+party means to make such an agreement or commitment not to enforce a
+patent against the party.
+
+  If you convey a covered work, knowingly relying on a patent license,
+and the Corresponding Source of the work is not available for anyone
+to copy, free of charge and under the terms of this License, through a
+publicly available network server or other readily accessible means,
+then you must either (1) cause the Corresponding Source to be so
+available, or (2) arrange to deprive yourself of the benefit of the
+patent license for this particular work, or (3) arrange, in a manner
+consistent with the requirements of this License, to extend the patent
+license to downstream recipients.  "Knowingly relying" means you have
+actual knowledge that, but for the patent license, your conveying the
+covered work in a country, or your recipient's use of the covered work
+in a country, would infringe one or more identifiable patents in that
+country that you have reason to believe are valid.
+
+  If, pursuant to or in connection with a single transaction or
+arrangement, you convey, or propagate by procuring conveyance of, a
+covered work, and grant a patent license to some of the parties
+receiving the covered work authorizing them to use, propagate, modify
+or convey a specific copy of the covered work, then the patent license
+you grant is automatically extended to all recipients of the covered
+work and works based on it.
+
+  A patent license is "discriminatory" if it does not include within
+the scope of its coverage, prohibits the exercise of, or is
+conditioned on the non-exercise of one or more of the rights that are
+specifically granted under this License.  You may not convey a covered
+work if you are a party to an arrangement with a third party that is
+in the business of distributing software, under which you make payment
+to the third party based on the extent of your activity of conveying
+the work, and under which the third party grants, to any of the
+parties who would receive the covered work from you, a discriminatory
+patent license (a) in connection with copies of the covered work
+conveyed by you (or copies made from those copies), or (b) primarily
+for and in connection with specific products or compilations that
+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

LICENSE.md

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so.  This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software.  The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable.  Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
+computer or modifying a private copy.  Propagation includes copying,
+distribution (with or without modification), making available to the
+public, and in some countries other activities as well.
+
+  To "convey" a work means any kind of propagation that enables other
+parties to make or receive copies.  Mere interaction with a user through
+a computer network, with no transfer of a copy, is not conveying.
+
+  An interactive user interface displays "Appropriate Legal Notices"
+to the extent that it includes a convenient and prominently visible
+feature that (1) displays an appropriate copyright notice, and (2)
+tells the user that there is no warranty for the work (except to the
+extent that warranties are provided), that licensees may convey the
+work under this License, and how to view a copy of this License.  If
+the interface presents a list of user commands or options, such as a
+menu, a prominent item in the list meets this criterion.
+
+  1. Source Code.
+
+  The "source code" for a work means the preferred form of the work
+for making modifications to it.  "Object code" means any non-source
+form of a work.
+
+  A "Standard Interface" means an interface that either is an official
+standard defined by a recognized standards body, or, in the case of
+interfaces specified for a particular programming language, one that
+is widely used among developers working in that language.
+
+  The "System Libraries" of an executable work include anything, other
+than the work as a whole, that (a) is included in the normal form of
+packaging a Major Component, but which is not part of that Major
+Component, and (b) serves only to enable use of the work with that
+Major Component, or to implement a Standard Interface for which an
+implementation is available to the public in source code form.  A
+"Major Component", in this context, means a major essential component
+(kernel, window system, and so on) of the specific operating system
+(if any) on which the executable work runs, or a compiler used to
+produce the work, or an object code interpreter used to run it.
+
+  The "Corresponding Source" for a work in object code form means all
+the source code needed to generate, install, and (for an executable
+work) run the object code and to modify the work, including scripts to
+control those activities.  However, it does not include the work's
+System Libraries, or general-purpose tools or generally available free
+programs which are used unmodified in performing those activities but
+which are not part of the work.  For example, Corresponding Source
+includes interface definition files associated with source files for
+the work, and the source code for shared libraries and dynamically
+linked subprograms that the work is specifically designed to require,
+such as by intimate data communication or control flow between those
+subprograms and other parts of the work.
+
+  The Corresponding Source need not include anything that users
+can regenerate automatically from other parts of the Corresponding
+Source.
+
+  The Corresponding Source for a work in source code form is that
+same work.
+
+  2. Basic Permissions.
+
+  All rights granted under this License are granted for the term of
+copyright on the Program, and are irrevocable provided the stated
+conditions are met.  This License explicitly affirms your unlimited
+permission to run the unmodified Program.  The output from running a
+covered work is covered by this License only if the output, given its
+content, constitutes a covered work.  This License acknowledges your
+rights of fair use or other equivalent, as provided by copyright law.
+
+  You may make, run and propagate covered works that you do not
+convey, without conditions so long as your license otherwise remains
+in force.  You may convey covered works to others for the sole purpose
+of having them make modifications exclusively for you, or provide you
+with facilities for running those works, provided that you comply with
+the terms of this License in conveying all material for which you do
+not control copyright.  Those thus making or running the covered works
+for you must do so exclusively on your behalf, under your direction
+and control, on terms that prohibit them from making any copies of
+your copyrighted material outside their relationship with you.
+
+  Conveying under any other circumstances is permitted solely under
+the conditions stated below.  Sublicensing is not allowed; section 10
+makes it unnecessary.
+
+  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
+
+  No covered work shall be deemed part of an effective technological
+measure under any applicable law fulfilling obligations under article
+11 of the WIPO copyright treaty adopted on 20 December 1996, or
+similar laws prohibiting or restricting circumvention of such
+measures.
+
+  When you convey a covered work, you waive any legal power to forbid
+circumvention of technological measures to the extent such circumvention
+is effected by exercising rights under this License with respect to
+the covered work, and you disclaim any intention to limit operation or
+modification of the work as a means of enforcing, against the work's
+users, your or third parties' legal rights to forbid circumvention of
+technological measures.
+
+  4. Conveying Verbatim Copies.
+
+  You may convey verbatim copies of the Program's source code as you
+receive it, in any medium, provided that you conspicuously and
+appropriately publish on each copy an appropriate copyright notice;
+keep intact all notices stating that this License and any
+non-permissive terms added in accord with section 7 apply to the code;
+keep intact all notices of the absence of any warranty; and give all
+recipients a copy of this License along with the Program.
+
+  You may charge any price or no price for each copy that you convey,
+and you may offer support or warranty protection for a fee.
+
+  5. Conveying Modified Source Versions.
+
+  You may convey a work based on the Program, or the modifications to
+produce it from the Program, in the form of source code under the
+terms of section 4, provided that you also meet all of these conditions:
+
+    a) The work must carry prominent notices stating that you modified
+    it, and giving a relevant date.
+
+    b) The work must carry prominent notices stating that it is
+    released under this License and any conditions added under section
+    7.  This requirement modifies the requirement in section 4 to
+    "keep intact all notices".
+
+    c) You must license the entire work, as a whole, under this
+    License to anyone who comes into possession of a copy.  This
+    License will therefore apply, along with any applicable section 7
+    additional terms, to the whole of the work, and all its parts,
+    regardless of how they are packaged.  This License gives no
+    permission to license the work in any other way, but it does not
+    invalidate such permission if you have separately received it.
+
+    d) If the work has interactive user interfaces, each must display
+    Appropriate Legal Notices; however, if the Program has interactive
+    interfaces that do not display Appropriate Legal Notices, your
+    work need not make them do so.
+
+  A compilation of a covered work with other separate and independent
+works, which are not by their nature extensions of the covered work,
+and which are not combined with it such as to form a larger program,
+in or on a volume of a storage or distribution medium, is called an
+"aggregate" if the compilation and its resulting copyright are not
+used to limit the access or legal rights of the compilation's users
+beyond what the individual works permit.  Inclusion of a covered work
+in an aggregate does not cause this License to apply to the other
+parts of the aggregate.
+
+  6. Conveying Non-Source Forms.
+
+  You may convey a covered work in object code form under the terms
+of sections 4 and 5, provided that you also convey the
+machine-readable Corresponding Source under the terms of this License,
+in one of these ways:
+
+    a) Convey the object code in, or embodied in, a physical product
+    (including a physical distribution medium), accompanied by the
+    Corresponding Source fixed on a durable physical medium
+    customarily used for software interchange.
+
+    b) Convey the object code in, or embodied in, a physical product
+    (including a physical distribution medium), accompanied by a
+    written offer, valid for at least three years and valid for as
+    long as you offer spare parts or customer support for that product
+    model, to give anyone who possesses the object code either (1) a
+    copy of the Corresponding Source for all the software in the
+    product that is covered by this License, on a durable physical
+    medium customarily used for software interchange, for a price no
+    more than your reasonable cost of physically performing this
+    conveying of source, or (2) access to copy the
+    Corresponding Source from a network server at no charge.
+
+    c) Convey individual copies of the object code with a copy of the
+    written offer to provide the Corresponding Source.  This
+    alternative is allowed only occasionally and noncommercially, and
+    only if you received the object code with such an offer, in accord
+    with subsection 6b.
+
+    d) Convey the object code by offering access from a designated
+    place (gratis or for a charge), and offer equivalent access to the
+    Corresponding Source in the same way through the same place at no
+    further charge.  You need not require recipients to copy the
+    Corresponding Source along with the object code.  If the place to
+    copy the object code is a network server, the Corresponding Source
+    may be on a different server (operated by you or a third party)
+    that supports equivalent copying facilities, provided you maintain
+    clear directions next to the object code saying where to find the
+    Corresponding Source.  Regardless of what server hosts the
+    Corresponding Source, you remain obligated to ensure that it is
+    available for as long as needed to satisfy these requirements.
+
+    e) Convey the object code using peer-to-peer transmission, provided
+    you inform other peers where the object code and Corresponding
+    Source of the work are being offered to the general public at no
+    charge under subsection 6d.
+
+  A separable portion of the object code, whose source code is excluded
+from the Corresponding Source as a System Library, need not be
+included in conveying the object code work.
+
+  A "User Product" is either (1) a "consumer product", which means any
+tangible personal property which is normally used for personal, family,
+or household purposes, or (2) anything designed or sold for incorporation
+into a dwelling.  In determining whether a product is a consumer product,
+doubtful cases shall be resolved in favor of coverage.  For a particular
+product received by a particular user, "normally used" refers to a
+typical or common use of that class of product, regardless of the status
+of the particular user or of the way in which the particular user
+actually uses, or expects or is expected to use, the product.  A product
+is a consumer product regardless of whether the product has substantial
+commercial, industrial or non-consumer uses, unless such uses represent
+the only significant mode of use of the product.
+
+  "Installation Information" for a User Product means any methods,
+procedures, authorization keys, or other information required to install
+and execute modified versions of a covered work in that User Product from
+a modified version of its Corresponding Source.  The information must
+suffice to ensure that the continued functioning of the modified object
+code is in no case prevented or interfered with solely because
+modification has been made.
+
+  If you convey an object code work under this section in, or with, or
+specifically for use in, a User Product, and the conveying occurs as
+part of a transaction in which the right of possession and use of the
+User Product is transferred to the recipient in perpetuity or for a
+fixed term (regardless of how the transaction is characterized), the
+Corresponding Source conveyed under this section must be accompanied
+by the Installation Information.  But this requirement does not apply
+if neither you nor any third party retains the ability to install
+modified object code on the User Product (for example, the work has
+been installed in ROM).
+
+  The requirement to provide Installation Information does not include a
+requirement to continue to provide support service, warranty, or updates
+for a work that has been modified or installed by the recipient, or for
+the User Product in which it has been modified or installed.  Access to a
+network may be denied when the modification itself materially and
+adversely affects the operation of the network or violates the rules and
+protocols for communication across the network.
+
+  Corresponding Source conveyed, and Installation Information provided,
+in accord with this section must be in a format that is publicly
+documented (and with an implementation available to the public in
+source code form), and must require no special password or key for
+unpacking, reading or copying.
+
+  7. Additional Terms.
+
+  "Additional permissions" are terms that supplement the terms of this
+License by making exceptions from one or more of its conditions.
+Additional permissions that are applicable to the entire Program shall
+be treated as though they were included in this License, to the extent
+that they are valid under applicable law.  If additional permissions
+apply only to part of the Program, that part may be used separately
+under those permissions, but the entire Program remains governed by
+this License without regard to the additional permissions.
+
+  When you convey a copy of a covered work, you may at your option
+remove any additional permissions from that copy, or from any part of
+it.  (Additional permissions may be written to require their own
+removal in certain cases when you modify the work.)  You may place
+additional permissions on material, added by you to a covered work,
+for which you have or can give appropriate copyright permission.
+
+  Notwithstanding any other provision of this License, for material you
+add to a covered work, you may (if authorized by the copyright holders of
+that material) supplement the terms of this License with terms:
+
+    a) Disclaiming warranty or limiting liability differently from the
+    terms of sections 15 and 16 of this License; or
+
+    b) Requiring preservation of specified reasonable legal notices or
+    author attributions in that material or in the Appropriate Legal
+    Notices displayed by works containing it; or
+
+    c) Prohibiting misrepresentation of the origin of that material, or
+    requiring that modified versions of such material be marked in
+    reasonable ways as different from the original version; or
+
+    d) Limiting the use for publicity purposes of names of licensors or
+    authors of the material; or
+
+    e) Declining to grant rights under trademark law for use of some
+    trade names, trademarks, or service marks; or
+
+    f) Requiring indemnification of licensors and authors of that
+    material by anyone who conveys the material (or modified versions of
+    it) with contractual assumptions of liability to the recipient, for
+    any liability that these contractual assumptions directly impose on
+    those licensors and authors.
+
+  All other non-permissive additional terms are considered "further
+restrictions" within the meaning of section 10.  If the Program as you
+received it, or any part of it, contains a notice stating that it is
+governed by this License along with a term that is a further
+restriction, you may remove that term.  If a license document contains
+a further restriction but permits relicensing or conveying under this
+License, you may add to a covered work material governed by the terms
+of that license document, provided that the further restriction does
+not survive such relicensing or conveying.
+
+  If you add terms to a covered work in accord with this section, you
+must place, in the relevant source files, a statement of the
+additional terms that apply to those files, or a notice indicating
+where to find the applicable terms.
+
+  Additional terms, permissive or non-permissive, may be stated in the
+form of a separately written license, or stated as exceptions;
+the above requirements apply either way.
+
+  8. Termination.
+
+  You may not propagate or modify a covered work except as expressly
+provided under this License.  Any attempt otherwise to propagate or
+modify it is void, and will automatically terminate your rights under
+this License (including any patent licenses granted under the third
+paragraph of section 11).
+
+  However, if you cease all violation of this License, then your
+license from a particular copyright holder is reinstated (a)
+provisionally, unless and until the copyright holder explicitly and
+finally terminates your license, and (b) permanently, if the copyright
+holder fails to notify you of the violation by some reasonable means
+prior to 60 days after the cessation.
+
+  Moreover, your license from a particular copyright holder is
+reinstated permanently if the copyright holder notifies you of the
+violation by some reasonable means, this is the first time you have
+received notice of violation of this License (for any work) from that
+copyright holder, and you cure the violation prior to 30 days after
+your receipt of the notice.
+
+  Termination of your rights under this section does not terminate the
+licenses of parties who have received copies or rights from you under
+this License.  If your rights have been terminated and not permanently
+reinstated, you do not qualify to receive new licenses for the same
+material under section 10.
+
+  9. Acceptance Not Required for Having Copies.
+
+  You are not required to accept this License in order to receive or
+run a copy of the Program.  Ancillary propagation of a covered work
+occurring solely as a consequence of using peer-to-peer transmission
+to receive a copy likewise does not require acceptance.  However,
+nothing other than this License grants you permission to propagate or
+modify any covered work.  These actions infringe copyright if you do
+not accept this License.  Therefore, by modifying or propagating a
+covered work, you indicate your acceptance of this License to do so.
+
+  10. Automatic Licensing of Downstream Recipients.
+
+  Each time you convey a covered work, the recipient automatically
+receives a license from the original licensors, to run, modify and
+propagate that work, subject to this License.  You are not responsible
+for enforcing compliance by third parties with this License.
+
+  An "entity transaction" is a transaction transferring control of an
+organization, or substantially all assets of one, or subdividing an
+organization, or merging organizations.  If propagation of a covered
+work results from an entity transaction, each party to that
+transaction who receives a copy of the work also receives whatever
+licenses to the work the party's predecessor in interest had or could
+give under the previous paragraph, plus a right to possession of the
+Corresponding Source of the work from the predecessor in interest, if
+the predecessor has it or can get it with reasonable efforts.
+
+  You may not impose any further restrictions on the exercise of the
+rights granted or affirmed under this License.  For example, you may
+not impose a license fee, royalty, or other charge for exercise of
+rights granted under this License, and you may not initiate litigation
+(including a cross-claim or counterclaim in a lawsuit) alleging that
+any patent claim is infringed by making, using, selling, offering for
+sale, or importing the Program or any portion of it.
+
+  11. Patents.
+
+  A "contributor" is a copyright holder who authorizes use under this
+License of the Program or a work on which the Program is based.  The
+work thus licensed is called the contributor's "contributor version".
+
+  A contributor's "essential patent claims" are all patent claims
+owned or controlled by the contributor, whether already acquired or
+hereafter acquired, that would be infringed by some manner, permitted
+by this License, of making, using, or selling its contributor version,
+but do not include claims that would be infringed only as a
+consequence of further modification of the contributor version.  For
+purposes of this definition, "control" includes the right to grant
+patent sublicenses in a manner consistent with the requirements of
+this License.
+
+  Each contributor grants you a non-exclusive, worldwide, royalty-free
+patent license under the contributor's essential patent claims, to
+make, use, sell, offer for sale, import and otherwise run, modify and
+propagate the contents of its contributor version.
+
+  In the following three paragraphs, a "patent license" is any express
+agreement or commitment, however denominated, not to enforce a patent
+(such as an express permission to practice a patent or covenant not to
+sue for patent infringement).  To "grant" such a patent license to a
+party means to make such an agreement or commitment not to enforce a
+patent against the party.
+
+  If you convey a covered work, knowingly relying on a patent license,
+and the Corresponding Source of the work is not available for anyone
+to copy, free of charge and under the terms of this License, through a
+publicly available network server or other readily accessible means,
+then you must either (1) cause the Corresponding Source to be so
+available, or (2) arrange to deprive yourself of the benefit of the
+patent license for this particular work, or (3) arrange, in a manner
+consistent with the requirements of this License, to extend the patent
+license to downstream recipients.  "Knowingly relying" means you have
+actual knowledge that, but for the patent license, your conveying the
+covered work in a country, or your recipient's use of the covered work
+in a country, would infringe one or more identifiable patents in that
+country that you have reason to believe are valid.
+
+  If, pursuant to or in connection with a single transaction or
+arrangement, you convey, or propagate by procuring conveyance of, a
+covered work, and grant a patent license to some of the parties
+receiving the covered work authorizing them to use, propagate, modify
+or convey a specific copy of the covered work, then the patent license
+you grant is automatically extended to all recipients of the covered
+work and works based on it.
+
+  A patent license is "discriminatory" if it does not include within
+the scope of its coverage, prohibits the exercise of, or is
+conditioned on the non-exercise of one or more of the rights that are
+specifically granted under this License.  You may not convey a covered
+work if you are a party to an arrangement with a third party that is
+in the business of distributing software, under which you make payment
+to the third party based on the extent of your activity of conveying
+the work, and under which the third party grants, to any of the
+parties who would receive the covered work from you, a discriminatory
+patent license (a) in connection with copies of the covered work
+conveyed by you (or copies made from those copies), or (b) primarily
+for and in connection with specific products or compilations that
+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,13 +1,13 @@
 ---
-title: Railway Detect
-emoji: 🐢
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 4.42.0
+title: Tri
+emoji: 👀
+colorFrom: indigo
+colorTo: red
+sdk: streamlit
+sdk_version: 1.37.1
 app_file: app.py
 pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

YOLO9_run-5-2.ipynb

@@ -0,0 +1,974 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mw7RoF7eRJEG",
+    "outputId": "1aa46197-e525-4227-b78a-5068873ebd65"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'YOLO9tr'...\n",
+      "remote: Enumerating objects: 489, done.\u001b[K\n",
+      "remote: Counting objects: 100% (198/198), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (150/150), done.\u001b[K\n",
+      "remote: Total 489 (delta 117), reused 102 (delta 40), pack-reused 291 (from 1)\u001b[K\n",
+      "Receiving objects: 100% (489/489), 79.21 MiB | 48.83 MiB/s, done.\n",
+      "Resolving deltas: 100% (225/225), done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!git clone https://github.com/Sompote/YOLO9tr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6KgJYQKfryz2",
+    "outputId": "f359b42d-f9d5-41ac-bbc6-d4c623672e92"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/workspace/YOLO9tr\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.\n",
+      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd /workspace/YOLO9tr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AnohaOgqq5rQ",
+    "outputId": "d4a78b77-33f3-43dd-af6e-c1ef5e32a8f9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting gitpython (from -r requirements.txt (line 5))\n",
+      "  Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB)\n",
+      "Requirement already satisfied: ipython in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 6)) (8.20.0)\n",
+      "Collecting matplotlib>=3.2.2 (from -r requirements.txt (line 7))\n",
+      "  Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
+      "Requirement already satisfied: numpy>=1.18.5 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 8)) (1.26.4)\n",
+      "Collecting opencv-python>=4.1.1 (from -r requirements.txt (line 9))\n",
+      "  Downloading opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
+      "Requirement already satisfied: Pillow>=7.1.2 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 10)) (10.3.0)\n",
+      "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 11)) (5.9.0)\n",
+      "Requirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 12)) (6.0.1)\n",
+      "Requirement already satisfied: requests>=2.23.0 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 13)) (2.32.2)\n",
+      "Collecting scipy>=1.4.1 (from -r requirements.txt (line 14))\n",
+      "  Downloading scipy-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hCollecting thop>=0.1.1 (from -r requirements.txt (line 15))\n",
+      "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl.metadata (2.7 kB)\n",
+      "Collecting opencv-contrib-python (from -r requirements.txt (line 17))\n",
+      "  Downloading opencv_contrib_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
+      "Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 18)) (2.3.1)\n",
+      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 19)) (0.18.1)\n",
+      "Requirement already satisfied: tqdm>=4.64.0 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 20)) (4.66.4)\n",
+      "Collecting tensorboard>=2.4.1 (from -r requirements.txt (line 24))\n",
+      "  Downloading tensorboard-2.17.1-py3-none-any.whl.metadata (1.6 kB)\n",
+      "Collecting pandas>=1.1.4 (from -r requirements.txt (line 29))\n",
+      "  Downloading pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n",
+      "Collecting seaborn>=0.11.0 (from -r requirements.txt (line 30))\n",
+      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Collecting albumentations>=1.0.3 (from -r requirements.txt (line 48))\n",
+      "  Downloading albumentations-1.4.14-py3-none-any.whl.metadata (38 kB)\n",
+      "Collecting pycocotools>=2.0 (from -r requirements.txt (line 49))\n",
+      "  Downloading pycocotools-2.0.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.1 kB)\n",
+      "Collecting gitdb<5,>=4.0.1 (from gitpython->-r requirements.txt (line 5))\n",
+      "  Downloading gitdb-4.0.11-py3-none-any.whl.metadata (1.2 kB)\n",
+      "Requirement already satisfied: decorator in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (5.1.1)\n",
+      "Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (0.18.1)\n",
+      "Requirement already satisfied: matplotlib-inline in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (0.1.6)\n",
+      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (3.0.43)\n",
+      "Requirement already satisfied: pygments>=2.4.0 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (2.15.1)\n",
+      "Requirement already satisfied: stack-data in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (0.2.0)\n",
+      "Requirement already satisfied: traitlets>=5 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (5.7.1)\n",
+      "Requirement already satisfied: exceptiongroup in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (1.2.0)\n",
+      "Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (4.8.0)\n",
+      "Collecting contourpy>=1.0.1 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB)\n",
+      "Collecting cycler>=0.10 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Collecting fonttools>=4.22.0 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading fonttools-4.53.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (162 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.6/162.6 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hCollecting kiwisolver>=1.3.1 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (6.4 kB)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->-r requirements.txt (line 7)) (23.2)\n",
+      "Collecting pyparsing>=2.3.1 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading pyparsing-3.1.2-py3-none-any.whl.metadata (5.1 kB)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->-r requirements.txt (line 7)) (2.9.0.post0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->-r requirements.txt (line 13)) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->-r requirements.txt (line 13)) (3.7)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->-r requirements.txt (line 13)) (2.2.1)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->-r requirements.txt (line 13)) (2024.2.2)\n",
+      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->-r requirements.txt (line 18)) (3.13.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch->-r requirements.txt (line 18)) (4.11.0)\n",
+      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->-r requirements.txt (line 18)) (1.12.1)\n",
+      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->-r requirements.txt (line 18)) (3.2.1)\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->-r requirements.txt (line 18)) (3.1.4)\n",
+      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch->-r requirements.txt (line 18)) (2024.6.0)\n",
+      "Collecting absl-py>=0.4 (from tensorboard>=2.4.1->-r requirements.txt (line 24))\n",
+      "  Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\n",
+      "Collecting grpcio>=1.48.2 (from tensorboard>=2.4.1->-r requirements.txt (line 24))\n",
+      "  Downloading grpcio-1.65.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.3 kB)\n",
+      "Collecting markdown>=2.6.8 (from tensorboard>=2.4.1->-r requirements.txt (line 24))\n",
+      "  Downloading Markdown-3.7-py3-none-any.whl.metadata (7.0 kB)\n",
+      "Collecting protobuf!=4.24.0,>=3.19.6 (from tensorboard>=2.4.1->-r requirements.txt (line 24))\n",
+      "  Downloading protobuf-5.27.3-cp38-abi3-manylinux2014_x86_64.whl.metadata (592 bytes)\n",
+      "Requirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard>=2.4.1->-r requirements.txt (line 24)) (69.5.1)\n",
+      "Requirement already satisfied: six>1.9 in /opt/conda/lib/python3.10/site-packages (from tensorboard>=2.4.1->-r requirements.txt (line 24)) (1.16.0)\n",
+      "Collecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard>=2.4.1->-r requirements.txt (line 24))\n",
+      "  Downloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl.metadata (1.1 kB)\n",
+      "Collecting werkzeug>=1.0.1 (from tensorboard>=2.4.1->-r requirements.txt (line 24))\n",
+      "  Downloading werkzeug-3.0.3-py3-none-any.whl.metadata (3.7 kB)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.4->-r requirements.txt (line 29)) (2024.1)\n",
+      "Collecting tzdata>=2022.7 (from pandas>=1.1.4->-r requirements.txt (line 29))\n",
+      "  Downloading tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
+      "Collecting scikit-image>=0.21.0 (from albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading scikit_image-0.24.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n",
+      "Collecting pydantic>=2.7.0 (from albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading pydantic-2.8.2-py3-none-any.whl.metadata (125 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m125.2/125.2 kB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hCollecting albucore>=0.0.13 (from albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading albucore-0.0.13-py3-none-any.whl.metadata (3.1 kB)\n",
+      "Collecting eval-type-backport (from albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading eval_type_backport-0.2.0-py3-none-any.whl.metadata (2.2 kB)\n",
+      "Collecting opencv-python-headless>=4.9.0.80 (from albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
+      "Requirement already satisfied: tomli>=2.0.1 in /opt/conda/lib/python3.10/site-packages (from albucore>=0.0.13->albumentations>=1.0.3->-r requirements.txt (line 48)) (2.0.1)\n",
+      "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython->-r requirements.txt (line 5))\n",
+      "  Downloading smmap-5.0.1-py3-none-any.whl.metadata (4.3 kB)\n",
+      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/lib/python3.10/site-packages (from jedi>=0.16->ipython->-r requirements.txt (line 6)) (0.8.3)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.10/site-packages (from pexpect>4.3->ipython->-r requirements.txt (line 6)) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/conda/lib/python3.10/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython->-r requirements.txt (line 6)) (0.2.5)\n",
+      "Collecting annotated-types>=0.4.0 (from pydantic>=2.7.0->albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading annotated_types-0.7.0-py3-none-any.whl.metadata (15 kB)\n",
+      "Collecting pydantic-core==2.20.1 (from pydantic>=2.7.0->albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading pydantic_core-2.20.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\n",
+      "Collecting imageio>=2.33 (from scikit-image>=0.21.0->albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading imageio-2.35.1-py3-none-any.whl.metadata (4.9 kB)\n",
+      "Collecting tifffile>=2022.8.12 (from scikit-image>=0.21.0->albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading tifffile-2024.8.10-py3-none-any.whl.metadata (31 kB)\n",
+      "Collecting lazy-loader>=0.4 (from scikit-image>=0.21.0->albumentations>=1.0.3->-r requirements.txt (line 48))\n",
+      "  Downloading lazy_loader-0.4-py3-none-any.whl.metadata (7.6 kB)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard>=2.4.1->-r requirements.txt (line 24)) (2.1.3)\n",
+      "Requirement already satisfied: executing in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython->-r requirements.txt (line 6)) (0.8.3)\n",
+      "Requirement already satisfied: asttokens in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython->-r requirements.txt (line 6)) (2.0.5)\n",
+      "Requirement already satisfied: pure-eval in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython->-r requirements.txt (line 6)) (0.2.2)\n",
+      "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->-r requirements.txt (line 18)) (1.3.0)\n",
+      "Downloading GitPython-3.1.43-py3-none-any.whl (207 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m33.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m86.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mta \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hDownloading opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62.5 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 MB\u001b[0m \u001b[31m59.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading scipy-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.1 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.1/41.1 MB\u001b[0m \u001b[31m60.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
+      "Downloading opencv_contrib_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (68.7 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m68.7/68.7 MB\u001b[0m \u001b[31m57.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading tensorboard-2.17.1-py3-none-any.whl (5.5 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m132.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.0 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.0/13.0 MB\u001b[0m \u001b[31m128.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n",
+      "\u001b[?25hDownloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m59.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading albumentations-1.4.14-py3-none-any.whl (177 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.0/178.0 kB\u001b[0m \u001b[31m43.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pycocotools-2.0.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (427 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m427.8/427.8 kB\u001b[0m \u001b[31m70.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading absl_py-2.1.0-py3-none-any.whl (133 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.7/133.7 kB\u001b[0m \u001b[31m40.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading albucore-0.0.13-py3-none-any.whl (8.5 kB)\n",
+      "Downloading contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (305 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m305.2/305.2 kB\u001b[0m \u001b[31m65.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
+      "Downloading fonttools-4.53.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.6/4.6 MB\u001b[0m \u001b[31m144.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading grpcio-1.65.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.7 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m139.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m128.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading Markdown-3.7-py3-none-any.whl (106 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.3/106.3 kB\u001b[0m \u001b[31m32.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.9 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.9/49.9 MB\u001b[0m \u001b[31m55.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading protobuf-5.27.3-cp38-abi3-manylinux2014_x86_64.whl (309 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.3/309.3 kB\u001b[0m \u001b[31m33.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pydantic-2.8.2-py3-none-any.whl (423 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m423.9/423.9 kB\u001b[0m \u001b[31m43.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pydantic_core-2.20.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m82.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pyparsing-3.1.2-py3-none-any.whl (103 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.2/103.2 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading scikit_image-0.24.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.9 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.9/14.9 MB\u001b[0m \u001b[31m92.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl (6.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.6/6.6 MB\u001b[0m \u001b[31m114.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hDownloading tzdata-2024.1-py2.py3-none-any.whl (345 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m345.4/345.4 kB\u001b[0m \u001b[31m55.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading werkzeug-3.0.3-py3-none-any.whl (227 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.3/227.3 kB\u001b[0m \u001b[31m45.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading eval_type_backport-0.2.0-py3-none-any.whl (5.9 kB)\n",
+      "Downloading annotated_types-0.7.0-py3-none-any.whl (13 kB)\n",
+      "Downloading imageio-2.35.1-py3-none-any.whl (315 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m315.4/315.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hDownloading lazy_loader-0.4-py3-none-any.whl (12 kB)\n",
+      "Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
+      "Downloading tifffile-2024.8.10-py3-none-any.whl (225 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.8/225.8 kB\u001b[0m \u001b[31m38.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: werkzeug, tzdata, tifffile, tensorboard-data-server, smmap, scipy, pyparsing, pydantic-core, protobuf, opencv-python-headless, opencv-python, opencv-contrib-python, markdown, lazy-loader, kiwisolver, imageio, grpcio, fonttools, eval-type-backport, cycler, contourpy, annotated-types, absl-py, tensorboard, scikit-image, pydantic, pandas, matplotlib, gitdb, albucore, thop, seaborn, pycocotools, gitpython, albumentations\n",
+      "Successfully installed absl-py-2.1.0 albucore-0.0.13 albumentations-1.4.14 annotated-types-0.7.0 contourpy-1.2.1 cycler-0.12.1 eval-type-backport-0.2.0 fonttools-4.53.1 gitdb-4.0.11 gitpython-3.1.43 grpcio-1.65.5 imageio-2.35.1 kiwisolver-1.4.5 lazy-loader-0.4 markdown-3.7 matplotlib-3.9.2 opencv-contrib-python-4.10.0.84 opencv-python-4.10.0.84 opencv-python-headless-4.10.0.84 pandas-2.2.2 protobuf-5.27.3 pycocotools-2.0.8 pydantic-2.8.2 pydantic-core-2.20.1 pyparsing-3.1.2 scikit-image-0.24.0 scipy-1.14.0 seaborn-0.13.2 smmap-5.0.1 tensorboard-2.17.1 tensorboard-data-server-0.7.2 thop-0.1.1.post2209072238 tifffile-2024.8.10 tzdata-2024.1 werkzeug-3.0.3\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -r  requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
+      "Hit:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n",
+      "Hit:3 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
+      "Get:4 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n",
+      "Fetched 129 kB in 1s (115 kB/s)    \n",
+      "Reading package lists... Done\n",
+      "Reading package lists... Done\n",
+      "Building dependency tree... Done\n",
+      "Reading state information... Done\n",
+      "The following additional packages will be installed:\n",
+      "  libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1\n",
+      "  libdrm2 libgl1 libgl1-amber-dri libgl1-mesa-dri libglapi-mesa libglvnd0\n",
+      "  libglx-mesa0 libglx0 libllvm15 libpciaccess0 libsensors-config libsensors5\n",
+      "  libx11-6 libx11-data libx11-xcb1 libxau6 libxcb-dri2-0 libxcb-dri3-0\n",
+      "  libxcb-glx0 libxcb-present0 libxcb-randr0 libxcb-shm0 libxcb-sync1\n",
+      "  libxcb-xfixes0 libxcb1 libxdmcp6 libxext6 libxfixes3 libxshmfence1\n",
+      "  libxxf86vm1\n",
+      "Suggested packages:\n",
+      "  pciutils lm-sensors\n",
+      "The following NEW packages will be installed:\n",
+      "  libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1\n",
+      "  libdrm2 libgl1 libgl1-amber-dri libgl1-mesa-dri libgl1-mesa-glx\n",
+      "  libglapi-mesa libglvnd0 libglx-mesa0 libglx0 libllvm15 libpciaccess0\n",
+      "  libsensors-config libsensors5 libx11-6 libx11-data libx11-xcb1 libxau6\n",
+      "  libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 libxcb-randr0\n",
+      "  libxcb-shm0 libxcb-sync1 libxcb-xfixes0 libxcb1 libxdmcp6 libxext6\n",
+      "  libxfixes3 libxshmfence1 libxxf86vm1\n",
+      "0 upgraded, 36 newly installed, 0 to remove and 7 not upgraded.\n",
+      "Need to get 40.1 MB of archives.\n",
+      "After this operation, 173 MB of additional disk space will be used.\n",
+      "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-common all 2.4.113-2~ubuntu0.22.04.1 [5450 B]\n",
+      "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm2 amd64 2.4.113-2~ubuntu0.22.04.1 [38.1 kB]\n",
+      "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxau6 amd64 1:1.0.9-1build5 [7634 B]\n",
+      "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxdmcp6 amd64 1:1.1.3-0ubuntu5 [10.9 kB]\n",
+      "Get:5 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb1 amd64 1.14-3ubuntu3 [49.0 kB]\n",
+      "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libx11-data all 2:1.7.5-1ubuntu0.3 [120 kB]\n",
+      "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libx11-6 amd64 2:1.7.5-1ubuntu0.3 [667 kB]\n",
+      "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxext6 amd64 2:1.3.4-1build1 [31.8 kB]\n",
+      "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-amdgpu1 amd64 2.4.113-2~ubuntu0.22.04.1 [19.9 kB]\n",
+      "Get:10 http://archive.ubuntu.com/ubuntu jammy/main amd64 libpciaccess0 amd64 0.16-3 [19.1 kB]\n",
+      "Get:11 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-intel1 amd64 2.4.113-2~ubuntu0.22.04.1 [66.7 kB]\n",
+      "Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-nouveau2 amd64 2.4.113-2~ubuntu0.22.04.1 [17.5 kB]\n",
+      "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-radeon1 amd64 2.4.113-2~ubuntu0.22.04.1 [21.6 kB]\n",
+      "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libglapi-mesa amd64 23.2.1-1ubuntu3.1~22.04.2 [37.1 kB]\n",
+      "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgl1-amber-dri amd64 21.3.9-0ubuntu1~22.04.1 [4218 kB]\n",
+      "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libllvm15 amd64 1:15.0.7-0ubuntu0.22.04.3 [25.4 MB]\n",
+      "Get:17 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsensors-config all 1:3.6.0-7ubuntu1 [5274 B]\n",
+      "Get:18 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsensors5 amd64 1:3.6.0-7ubuntu1 [26.3 kB]\n",
+      "Get:19 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-dri3-0 amd64 1.14-3ubuntu3 [6968 B]\n",
+      "Get:20 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgl1-mesa-dri amd64 23.2.1-1ubuntu3.1~22.04.2 [8860 kB]\n",
+      "Get:21 http://archive.ubuntu.com/ubuntu jammy/main amd64 libglvnd0 amd64 1.4.0-1 [73.6 kB]\n",
+      "Get:22 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libx11-xcb1 amd64 2:1.7.5-1ubuntu0.3 [7802 B]\n",
+      "Get:23 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-dri2-0 amd64 1.14-3ubuntu3 [7206 B]\n",
+      "Get:24 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-glx0 amd64 1.14-3ubuntu3 [25.9 kB]\n",
+      "Get:25 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-present0 amd64 1.14-3ubuntu3 [5734 B]\n",
+      "Get:26 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-randr0 amd64 1.14-3ubuntu3 [18.3 kB]\n",
+      "Get:27 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-shm0 amd64 1.14-3ubuntu3 [5780 B]\n",
+      "Get:28 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-sync1 amd64 1.14-3ubuntu3 [9416 B]\n",
+      "Get:29 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-xfixes0 amd64 1.14-3ubuntu3 [9996 B]\n",
+      "Get:30 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxfixes3 amd64 1:6.0.0-1 [11.7 kB]\n",
+      "Get:31 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxshmfence1 amd64 1.3-1build4 [5394 B]\n",
+      "Get:32 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxxf86vm1 amd64 1:1.1.4-1build3 [10.4 kB]\n",
+      "Get:33 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libglx-mesa0 amd64 23.2.1-1ubuntu3.1~22.04.2 [158 kB]\n",
+      "Get:34 http://archive.ubuntu.com/ubuntu jammy/main amd64 libglx0 amd64 1.4.0-1 [41.0 kB]\n",
+      "Get:35 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgl1 amd64 1.4.0-1 [110 kB]\n",
+      "Get:36 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libgl1-mesa-glx amd64 23.0.4-0ubuntu1~22.04.1 [5584 B]\n",
+      "Fetched 40.1 MB in 1s (32.9 MB/s)\n",
+      "debconf: delaying package configuration, since apt-utils is not installed\n",
+      "Selecting previously unselected package libdrm-common.\n",
+      "(Reading database ... 16755 files and directories currently installed.)\n",
+      "Preparing to unpack .../00-libdrm-common_2.4.113-2~ubuntu0.22.04.1_all.deb ...\n",
+      "Unpacking libdrm-common (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libdrm2:amd64.\n",
+      "Preparing to unpack .../01-libdrm2_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm2:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libxau6:amd64.\n",
+      "Preparing to unpack .../02-libxau6_1%3a1.0.9-1build5_amd64.deb ...\n",
+      "Unpacking libxau6:amd64 (1:1.0.9-1build5) ...\n",
+      "Selecting previously unselected package libxdmcp6:amd64.\n",
+      "Preparing to unpack .../03-libxdmcp6_1%3a1.1.3-0ubuntu5_amd64.deb ...\n",
+      "Unpacking libxdmcp6:amd64 (1:1.1.3-0ubuntu5) ...\n",
+      "Selecting previously unselected package libxcb1:amd64.\n",
+      "Preparing to unpack .../04-libxcb1_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb1:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libx11-data.\n",
+      "Preparing to unpack .../05-libx11-data_2%3a1.7.5-1ubuntu0.3_all.deb ...\n",
+      "Unpacking libx11-data (2:1.7.5-1ubuntu0.3) ...\n",
+      "Selecting previously unselected package libx11-6:amd64.\n",
+      "Preparing to unpack .../06-libx11-6_2%3a1.7.5-1ubuntu0.3_amd64.deb ...\n",
+      "Unpacking libx11-6:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
+      "Selecting previously unselected package libxext6:amd64.\n",
+      "Preparing to unpack .../07-libxext6_2%3a1.3.4-1build1_amd64.deb ...\n",
+      "Unpacking libxext6:amd64 (2:1.3.4-1build1) ...\n",
+      "Selecting previously unselected package libdrm-amdgpu1:amd64.\n",
+      "Preparing to unpack .../08-libdrm-amdgpu1_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm-amdgpu1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libpciaccess0:amd64.\n",
+      "Preparing to unpack .../09-libpciaccess0_0.16-3_amd64.deb ...\n",
+      "Unpacking libpciaccess0:amd64 (0.16-3) ...\n",
+      "Selecting previously unselected package libdrm-intel1:amd64.\n",
+      "Preparing to unpack .../10-libdrm-intel1_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm-intel1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libdrm-nouveau2:amd64.\n",
+      "Preparing to unpack .../11-libdrm-nouveau2_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm-nouveau2:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libdrm-radeon1:amd64.\n",
+      "Preparing to unpack .../12-libdrm-radeon1_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm-radeon1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libglapi-mesa:amd64.\n",
+      "Preparing to unpack .../13-libglapi-mesa_23.2.1-1ubuntu3.1~22.04.2_amd64.deb ...\n",
+      "Unpacking libglapi-mesa:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Selecting previously unselected package libgl1-amber-dri:amd64.\n",
+      "Preparing to unpack .../14-libgl1-amber-dri_21.3.9-0ubuntu1~22.04.1_amd64.deb ...\n",
+      "Unpacking libgl1-amber-dri:amd64 (21.3.9-0ubuntu1~22.04.1) ...\n",
+      "Selecting previously unselected package libllvm15:amd64.\n",
+      "Preparing to unpack .../15-libllvm15_1%3a15.0.7-0ubuntu0.22.04.3_amd64.deb ...\n",
+      "Unpacking libllvm15:amd64 (1:15.0.7-0ubuntu0.22.04.3) ...\n",
+      "Selecting previously unselected package libsensors-config.\n",
+      "Preparing to unpack .../16-libsensors-config_1%3a3.6.0-7ubuntu1_all.deb ...\n",
+      "Unpacking libsensors-config (1:3.6.0-7ubuntu1) ...\n",
+      "Selecting previously unselected package libsensors5:amd64.\n",
+      "Preparing to unpack .../17-libsensors5_1%3a3.6.0-7ubuntu1_amd64.deb ...\n",
+      "Unpacking libsensors5:amd64 (1:3.6.0-7ubuntu1) ...\n",
+      "Selecting previously unselected package libxcb-dri3-0:amd64.\n",
+      "Preparing to unpack .../18-libxcb-dri3-0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-dri3-0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libgl1-mesa-dri:amd64.\n",
+      "Preparing to unpack .../19-libgl1-mesa-dri_23.2.1-1ubuntu3.1~22.04.2_amd64.deb ...\n",
+      "Unpacking libgl1-mesa-dri:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Selecting previously unselected package libglvnd0:amd64.\n",
+      "Preparing to unpack .../20-libglvnd0_1.4.0-1_amd64.deb ...\n",
+      "Unpacking libglvnd0:amd64 (1.4.0-1) ...\n",
+      "Selecting previously unselected package libx11-xcb1:amd64.\n",
+      "Preparing to unpack .../21-libx11-xcb1_2%3a1.7.5-1ubuntu0.3_amd64.deb ...\n",
+      "Unpacking libx11-xcb1:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
+      "Selecting previously unselected package libxcb-dri2-0:amd64.\n",
+      "Preparing to unpack .../22-libxcb-dri2-0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-dri2-0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-glx0:amd64.\n",
+      "Preparing to unpack .../23-libxcb-glx0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-glx0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-present0:amd64.\n",
+      "Preparing to unpack .../24-libxcb-present0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-present0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-randr0:amd64.\n",
+      "Preparing to unpack .../25-libxcb-randr0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-randr0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-shm0:amd64.\n",
+      "Preparing to unpack .../26-libxcb-shm0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-shm0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-sync1:amd64.\n",
+      "Preparing to unpack .../27-libxcb-sync1_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-sync1:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-xfixes0:amd64.\n",
+      "Preparing to unpack .../28-libxcb-xfixes0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-xfixes0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxfixes3:amd64.\n",
+      "Preparing to unpack .../29-libxfixes3_1%3a6.0.0-1_amd64.deb ...\n",
+      "Unpacking libxfixes3:amd64 (1:6.0.0-1) ...\n",
+      "Selecting previously unselected package libxshmfence1:amd64.\n",
+      "Preparing to unpack .../30-libxshmfence1_1.3-1build4_amd64.deb ...\n",
+      "Unpacking libxshmfence1:amd64 (1.3-1build4) ...\n",
+      "Selecting previously unselected package libxxf86vm1:amd64.\n",
+      "Preparing to unpack .../31-libxxf86vm1_1%3a1.1.4-1build3_amd64.deb ...\n",
+      "Unpacking libxxf86vm1:amd64 (1:1.1.4-1build3) ...\n",
+      "Selecting previously unselected package libglx-mesa0:amd64.\n",
+      "Preparing to unpack .../32-libglx-mesa0_23.2.1-1ubuntu3.1~22.04.2_amd64.deb ...\n",
+      "Unpacking libglx-mesa0:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Selecting previously unselected package libglx0:amd64.\n",
+      "Preparing to unpack .../33-libglx0_1.4.0-1_amd64.deb ...\n",
+      "Unpacking libglx0:amd64 (1.4.0-1) ...\n",
+      "Selecting previously unselected package libgl1:amd64.\n",
+      "Preparing to unpack .../34-libgl1_1.4.0-1_amd64.deb ...\n",
+      "Unpacking libgl1:amd64 (1.4.0-1) ...\n",
+      "Selecting previously unselected package libgl1-mesa-glx:amd64.\n",
+      "Preparing to unpack .../35-libgl1-mesa-glx_23.0.4-0ubuntu1~22.04.1_amd64.deb ...\n",
+      "Unpacking libgl1-mesa-glx:amd64 (23.0.4-0ubuntu1~22.04.1) ...\n",
+      "Setting up libpciaccess0:amd64 (0.16-3) ...\n",
+      "Setting up libxau6:amd64 (1:1.0.9-1build5) ...\n",
+      "Setting up libxdmcp6:amd64 (1:1.1.3-0ubuntu5) ...\n",
+      "Setting up libxcb1:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libxcb-xfixes0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libglvnd0:amd64 (1.4.0-1) ...\n",
+      "Setting up libxcb-glx0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libsensors-config (1:3.6.0-7ubuntu1) ...\n",
+      "Setting up libxcb-shm0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libxcb-present0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libx11-data (2:1.7.5-1ubuntu0.3) ...\n",
+      "Setting up libxcb-sync1:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libsensors5:amd64 (1:3.6.0-7ubuntu1) ...\n",
+      "Setting up libglapi-mesa:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Setting up libxcb-dri2-0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libxshmfence1:amd64 (1.3-1build4) ...\n",
+      "Setting up libxcb-randr0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libllvm15:amd64 (1:15.0.7-0ubuntu0.22.04.3) ...\n",
+      "Setting up libx11-6:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
+      "Setting up libdrm-common (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libxcb-dri3-0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libx11-xcb1:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
+      "Setting up libxext6:amd64 (2:1.3.4-1build1) ...\n",
+      "Setting up libxxf86vm1:amd64 (1:1.1.4-1build3) ...\n",
+      "Setting up libxfixes3:amd64 (1:6.0.0-1) ...\n",
+      "Setting up libdrm2:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libdrm-amdgpu1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libdrm-nouveau2:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libdrm-radeon1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libdrm-intel1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libgl1-mesa-dri:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Setting up libgl1-amber-dri:amd64 (21.3.9-0ubuntu1~22.04.1) ...\n",
+      "Setting up libglx-mesa0:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Setting up libglx0:amd64 (1.4.0-1) ...\n",
+      "Setting up libgl1:amd64 (1.4.0-1) ...\n",
+      "Setting up libgl1-mesa-glx:amd64 (23.0.4-0ubuntu1~22.04.1) ...\n",
+      "Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n",
+      "yes: standard output: Broken pipe\n"
+     ]
+    }
+   ],
+   "source": [
+    "!sudo apt-get update\n",
+    "!yes | sudo apt-get install libgl1-mesa-glx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/workspace/YOLO9tr\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd /workspace/YOLO9tr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting Pillow==9.5.0\n",
+      "  Downloading Pillow-9.5.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (9.5 kB)\n",
+      "Downloading Pillow-9.5.0-cp310-cp310-manylinux_2_28_x86_64.whl (3.4 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m47.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: Pillow\n",
+      "  Attempting uninstall: Pillow\n",
+      "    Found existing installation: pillow 10.3.0\n",
+      "    Uninstalling pillow-10.3.0:\n",
+      "      Successfully uninstalled pillow-10.3.0\n",
+      "Successfully installed Pillow-9.5.0\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install Pillow==9.5.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1msegment/train: \u001b[0mweights=, cfg=/workspace/YOLO9_KMUTT/yolov9/models/detect/yolov9-kmuttX.yaml, data=/workspace/help_me/data.yaml, hyp=hyp.scratch-high.yaml, epochs=500, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=yolov9-c, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, close_mosaic=15, mask_ratio=4, no_overlap=False\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/YOLO9tr/segment/train.py\", line 646, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/YOLO9tr/segment/train.py\", line 517, in main\n",
+      "    check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project)  # checks\n",
+      "  File \"/workspace/YOLO9tr/utils/general.py\", line 452, in check_yaml\n",
+      "    return check_file(file, suffix)\n",
+      "  File \"/workspace/YOLO9tr/utils/general.py\", line 478, in check_file\n",
+      "    assert len(files), f'File not found: {file}'  # assert file was found\n",
+      "AssertionError: File not found: /workspace/YOLO9_KMUTT/yolov9/models/detect/yolov9-kmuttX.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python /workspace/YOLO9tr/segment/train.py --workers 8 --device 0 --batch 4 --data '/workspace/help_me/data.yaml' --img 640 --cfg /workspace/YOLO9_KMUTT/yolov9/models/detect/yolov9-kmuttX.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml  --epochs 500 --close-mosaic 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jjB9EA9yRCyR",
+    "outputId": "2e388fee-6ec5-492d-b698-55d2d1cec2f2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1msegment/train: \u001b[0mweights=, cfg=/workspace/YOLO9tr/models/segment/gelan-c-seg.yaml, data=/workspace/help_me/data.yaml, hyp=hyp.scratch-high.yaml, epochs=200, batch_size=20, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=yolov9-seg, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, close_mosaic=15, mask_ratio=4, no_overlap=False\n",
+      "YOLO 🚀 ceb4d07 Python-3.10.14 torch-2.3.1 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
+      "\n",
+      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, cls_pw=1.0, obj=0.7, obj_pw=1.0, dfl=1.5, iou_t=0.2, anchor_t=5.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.3\n",
+      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n",
+      "Overriding model.yaml nc=80 with nc=2\n",
+      "\n",
+      "                 from  n    params  module                                  arguments                     \n",
+      "  0                -1  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n",
+      "  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
+      "  2                -1  1    212864  models.common.RepNCSPELAN4              [128, 256, 128, 64, 1]        \n",
+      "  3                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      "  4                -1  1    847616  models.common.RepNCSPELAN4              [256, 512, 256, 128, 1]       \n",
+      "  5                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      "  6                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n",
+      "  7                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      "  8                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n",
+      "  9                -1  1    656896  models.common.SPPELAN                   [512, 512, 256]               \n",
+      " 10                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 11           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
+      " 12                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n",
+      " 13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 14           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
+      " 15                -1  1    912640  models.common.RepNCSPELAN4              [1024, 256, 256, 128, 1]      \n",
+      " 16                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      " 17          [-1, 12]  1         0  models.common.Concat                    [1]                           \n",
+      " 18                -1  1   2988544  models.common.RepNCSPELAN4              [768, 512, 512, 256, 1]       \n",
+      " 19                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      " 20           [-1, 9]  1         0  models.common.Concat                    [1]                           \n",
+      " 21                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n",
+      " 22      [15, 18, 21]  1   7628150  models.yolo.Segment                     [2, 32, 256, [256, 512, 512]] \n",
+      "gelan-c-seg summary: 657 layers, 27574582 parameters, 27574566 gradients, 145.7 GFLOPs\n",
+      "\n",
+      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
+      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 163 weight(decay=0.0), 173 weight(decay=0.00046875), 172 bias\n",
+      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /workspace/help_me/train/labels.cache... 6428 images, 18 backgro\u001b[0m\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mScanning /workspace/help_me/valid/labels.cache... 307 images, 1 backgrounds\u001b[0m\n",
+      "Plotting labels to runs/train-seg/yolov9-seg16/labels.jpg... \n",
+      "Image sizes 640 train, 640 val\n",
+      "Using 8 dataloader workers\n",
+      "Logging results to \u001b[1mruns/train-seg/yolov9-seg16\u001b[0m\n",
+      "Starting training for 200 epochs...\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      0/199      18.7G      3.376      2.361      3.789        4.2         46   \n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        307        665    0.00447      0.588     0.0371     0.0107    0.00259      0.339    0.00786    0.00159\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      1/199      19.8G       3.35      1.516      3.408      3.821         39   \n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        307        665      0.528      0.329     0.0442    0.00933      0.515      0.184     0.0162    0.00312\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      2/199      19.8G      2.493      1.427      2.813      2.893         43   \n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        307        665       0.71      0.224      0.205     0.0997      0.648      0.114     0.0771      0.019\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      3/199      19.8G      1.876      1.357      2.274      2.239         47   \n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        307        665      0.703      0.331       0.37      0.229      0.779       0.24      0.247     0.0752\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      4/199      19.8G      1.631      1.275      1.975      1.974         53   \n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        307        665      0.472      0.403      0.382      0.248      0.432      0.302      0.271     0.0936\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      5/199      19.8G      1.517      1.238      1.829      1.856         47   \n",
+      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
+      "                   all        307        665       0.46      0.409      0.415       0.29      0.413      0.332      0.296      0.104\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      6/199      19.8G      1.456      1.193      1.749      1.788        138   "
+     ]
+    }
+   ],
+   "source": [
+    "!python /workspace/YOLO9tr/segment/train.py --workers 8 --device 0 --batch 20 --data '/workspace/help_me/data.yaml' \\\n",
+    "--img 640 --cfg /workspace/YOLO9tr/models/segment/gelan-c-seg.yaml --weights '' --name yolov9-seg --hyp hyp.scratch-high.yaml  \\\n",
+    "--epochs 200 --close-mosaic 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "VRMBngDDsIok"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "W0819 12:52:56.548000 140419195352896 torch/distributed/run.py:757] \n",
+      "W0819 12:52:56.548000 140419195352896 torch/distributed/run.py:757] *****************************************\n",
+      "W0819 12:52:56.548000 140419195352896 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \n",
+      "W0819 12:52:56.548000 140419195352896 torch/distributed/run.py:757] *****************************************\n",
+      "\u001b[34m\u001b[1msegment/train: \u001b[0mweights=, cfg=/workspace/YOLO9tr/models/segment/gelan-c-seg.yaml, data=/workspace/help_me/data.yaml, hyp=hyp.scratch-high.yaml, epochs=500, batch_size=30, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0,1, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=True, workers=8, project=runs/train-seg, name=yolov9-seg, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, close_mosaic=15, mask_ratio=4, no_overlap=False\n",
+      "YOLO 🚀 ceb4d07 Python-3.10.14 torch-2.3.1 CUDA:0 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
+      "                                           CUDA:1 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
+      "\n",
+      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, cls_pw=1.0, obj=0.7, obj_pw=1.0, dfl=1.5, iou_t=0.2, anchor_t=5.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.3\n",
+      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n",
+      "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
+      "100%|████████████████████████████████████████| 755k/755k [00:00<00:00, 32.8MB/s]\n",
+      "Overriding model.yaml nc=80 with nc=2\n",
+      "\n",
+      "                 from  n    params  module                                  arguments                     \n",
+      "  0                -1  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n",
+      "  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
+      "  2                -1  1    212864  models.common.RepNCSPELAN4              [128, 256, 128, 64, 1]        \n",
+      "  3                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      "  4                -1  1    847616  models.common.RepNCSPELAN4              [256, 512, 256, 128, 1]       \n",
+      "  5                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      "  6                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n",
+      "  7                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      "  8                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n",
+      "  9                -1  1    656896  models.common.SPPELAN                   [512, 512, 256]               \n",
+      " 10                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 11           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
+      " 12                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n",
+      " 13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 14           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
+      " 15                -1  1    912640  models.common.RepNCSPELAN4              [1024, 256, 256, 128, 1]      \n",
+      " 16                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      " 17          [-1, 12]  1         0  models.common.Concat                    [1]                           \n",
+      " 18                -1  1   2988544  models.common.RepNCSPELAN4              [768, 512, 512, 256, 1]       \n",
+      " 19                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      " 20           [-1, 9]  1         0  models.common.Concat                    [1]                           \n",
+      " 21                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n",
+      " 22      [15, 18, 21]  1   7628150  models.yolo.Segment                     [2, 32, 256, [256, 512, 512]] \n",
+      "gelan-c-seg summary: 657 layers, 27574582 parameters, 27574566 gradients, 145.7 GFLOPs\n",
+      "\n",
+      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
+      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 163 weight(decay=0.0), 173 weight(decay=0.00046875), 172 bias\n",
+      "Using SyncBatchNorm()\n",
+      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /workspace/help_me/train/labels... 6428 images, 18 backgrounds, \u001b[0m\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /workspace/help_me/train/labels.cache\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mScanning /workspace/help_me/valid/labels... 307 images, 1 backgrounds, 0 co\u001b[0m\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /workspace/help_me/valid/labels.cache\n",
+      "Plotting labels to runs/train-seg/yolov9-seg2/labels.jpg... \n",
+      "Image sizes 640 train, 640 val\n",
+      "Using 16 dataloader workers\n",
+      "Logging results to \u001b[1mruns/train-seg/yolov9-seg2\u001b[0m\n",
+      "Starting training for 500 epochs...\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      0/499      12.2G      3.356      2.644      3.818      4.234        127   ^C\n",
+      "W0819 12:54:10.488000 140419195352896 torch/distributed/elastic/agent/server/api.py:741] Received Signals.SIGINT death signal, shutting down workers\n",
+      "W0819 12:54:10.489000 140419195352896 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1257 closing signal SIGINT\n",
+      "W0819 12:54:10.489000 140419195352896 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1258 closing signal SIGINT\n",
+      "      0/499      12.2G      3.356      2.645      3.817      4.234         96   \n",
+      "[rank1]: Traceback (most recent call last):\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/segment/train.py\", line 646, in <module>\n",
+      "[rank1]:     main(opt)\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/segment/train.py\", line 542, in main\n",
+      "[rank1]:     train(opt.hyp, opt, device, callbacks)\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/segment/train.py\", line 296, in train\n",
+      "[rank1]:     pred = model(imgs)  # forward\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py\", line 1593, in forward\n",
+      "[rank1]:     else self._run_ddp_forward(*inputs, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py\", line 1411, in _run_ddp_forward\n",
+      "[rank1]:     return self.module(*inputs, **kwargs)  # type: ignore[index]\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/models/yolo.py\", line 633, in forward\n",
+      "[rank1]:     return self._forward_once(x, profile, visualize)  # single-scale inference, train\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/models/yolo.py\", line 533, in _forward_once\n",
+      "[rank1]:     x = m(x)  # run\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/models/common.py\", line 613, in forward\n",
+      "[rank1]:     y.extend((m(y[-1])) for m in [self.cv2, self.cv3])\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/models/common.py\", line 613, in <genexpr>\n",
+      "[rank1]:     y.extend((m(y[-1])) for m in [self.cv2, self.cv3])\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "[rank1]:     input = module(input)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/models/common.py\", line 384, in forward\n",
+      "[rank1]:     return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "[rank1]:     input = module(input)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/models/common.py\", line 309, in forward\n",
+      "[rank1]:     return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/workspace/YOLO9tr/models/common.py\", line 54, in forward\n",
+      "[rank1]:     return self.act(self.bn(self.conv(x)))\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank1]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank1]:     return forward_call(*args, **kwargs)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py\", line 460, in forward\n",
+      "[rank1]:     return self._conv_forward(input, self.weight, self.bias)\n",
+      "[rank1]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py\", line 456, in _conv_forward\n",
+      "[rank1]:     return F.conv2d(input, weight, bias, self.stride,\n",
+      "[rank1]: KeyboardInterrupt\n",
+      "[rank0]: Exception in thread Thread-10 (_pin_memory_loop):\n",
+      "[rank0]: Traceback (most recent call last):\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
+      "[rank0]:     self.run()\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/threading.py\", line 953, in run\n",
+      "[rank0]:     self._target(*self._args, **self._kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py\", line 54, in _pin_memory_loop\n",
+      "[rank0]:     do_one_step()\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py\", line 31, in do_one_step\n",
+      "[rank0]:     r = in_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/multiprocessing/queues.py\", line 122, in get\n",
+      "[rank0]: Traceback (most recent call last):\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/segment/train.py\", line 646, in <module>\n",
+      "[rank0]:     main(opt)\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/segment/train.py\", line 542, in main\n",
+      "[rank0]:     train(opt.hyp, opt, device, callbacks)\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/segment/train.py\", line 296, in train\n",
+      "[rank0]:     pred = model(imgs)  # forward\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py\", line 1593, in forward\n",
+      "[rank0]:     else self._run_ddp_forward(*inputs, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py\", line 1411, in _run_ddp_forward\n",
+      "[rank0]:     return self.module(*inputs, **kwargs)  # type: ignore[index]\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/models/yolo.py\", line 633, in forward\n",
+      "[rank0]:     return self._forward_once(x, profile, visualize)  # single-scale inference, train\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/models/yolo.py\", line 533, in _forward_once\n",
+      "[rank0]:     x = m(x)  # run\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/models/common.py\", line 613, in forward\n",
+      "[rank0]:     y.extend((m(y[-1])) for m in [self.cv2, self.cv3])\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/models/common.py\", line 613, in <genexpr>\n",
+      "[rank0]:     y.extend((m(y[-1])) for m in [self.cv2, self.cv3])\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "[rank0]:     input = module(input)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/models/common.py\", line 384, in forward\n",
+      "[rank0]:     return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "[rank0]:     input = module(input)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/models/common.py\", line 309, in forward\n",
+      "[rank0]:     return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/models/common.py\", line 111, in forward\n",
+      "[rank0]:     return self.act(self.conv1(x) + self.conv2(x) + id_out)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/workspace/YOLO9tr/models/common.py\", line 54, in forward\n",
+      "[rank0]:     return self.act(self.bn(self.conv(x)))\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
+      "[rank0]:     return self._call_impl(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
+      "[rank0]:     return forward_call(*args, **kwargs)\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py\", line 776, in forward\n",
+      "[rank0]:     return sync_batch_norm.apply(\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/autograd/function.py\", line 598, in apply\n",
+      "[rank0]:     return super().apply(*args, **kwargs)  # type: ignore[misc]\n",
+      "[rank0]:   File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/_functions.py\", line 89, in forward\n",
+      "[rank0]:     counts = count_all.view(-1)\n",
+      "[rank0]: KeyboardInterrupt\n"
+     ]
+    }
+   ],
+   "source": [
+    "!torchrun  --nproc_per_node 2 --master_port 9527 /workspace/YOLO9tr/segment/train.py  \\\n",
+    "--workers 8 --device 0,1 --sync-bn --batch 30 --data '/workspace/help_me/data.yaml' \\\n",
+    "--img 640 --cfg /workspace/YOLO9tr/models/segment/gelan-c-seg.yaml  --weights '' --name yolov9-seg --hyp hyp.scratch-high.yaml \\\n",
+    "--epochs 500 --close-mosaic 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "W0819 12:54:19.648000 139777630365504 torch/distributed/run.py:757] \n",
+      "W0819 12:54:19.648000 139777630365504 torch/distributed/run.py:757] *****************************************\n",
+      "W0819 12:54:19.648000 139777630365504 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \n",
+      "W0819 12:54:19.648000 139777630365504 torch/distributed/run.py:757] *****************************************\n"
+     ]
+    }
+   ],
+   "source": [
+    "!torchrun  --nproc_per_node 2 --master_port 9527 train_dual.py  \\\n",
+    "--workers 8 --device 0,1 --sync-bn --batch 40 --data '/workspace/road damage/data.yaml'  \\\n",
+    "--img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml \\\n",
+    "--min-items 0 --epochs 500 --close-mosaic 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!torchrun  --nproc_per_node 2 --master_port 9527 train_dual.py  \\\n",
+    "--workers 8 --device 0,1 --sync-bn --batch 30 --data '/workspace/road damage/data.yaml'  \\\n",
+    "--img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml \\\n",
+    "--min-items 0 --epochs 500 --close-mosaic 15"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "L4",
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

YOLO9_run.ipynb

@@ -0,0 +1,1123 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mw7RoF7eRJEG",
+    "outputId": "1aa46197-e525-4227-b78a-5068873ebd65"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'YOLO9_KMUTT'...\n",
+      "remote: Enumerating objects: 182, done.\u001b[K\n",
+      "remote: Counting objects: 100% (182/182), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (132/132), done.\u001b[K\n",
+      "remote: Total 182 (delta 49), reused 179 (delta 46), pack-reused 0\u001b[K\n",
+      "Receiving objects: 100% (182/182), 2.27 MiB | 8.47 MiB/s, done.\n",
+      "Resolving deltas: 100% (49/49), done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!git clone https://github.com/Sompote/YOLO9_KMUTT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6KgJYQKfryz2",
+    "outputId": "f359b42d-f9d5-41ac-bbc6-d4c623672e92"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/workspace/YOLO9_KMUTT/yolov9\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd /workspace/YOLO9_KMUTT/yolov9/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AnohaOgqq5rQ",
+    "outputId": "d4a78b77-33f3-43dd-af6e-c1ef5e32a8f9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting gitpython (from -r requirements.txt (line 5))\n",
+      "  Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB)\n",
+      "Requirement already satisfied: ipython in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 6)) (8.20.0)\n",
+      "Collecting matplotlib>=3.2.2 (from -r requirements.txt (line 7))\n",
+      "  Downloading matplotlib-3.9.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
+      "Requirement already satisfied: numpy>=1.18.5 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 8)) (1.26.3)\n",
+      "Collecting opencv-python>=4.1.1 (from -r requirements.txt (line 9))\n",
+      "  Downloading opencv_python-4.10.0.82-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
+      "Requirement already satisfied: Pillow>=7.1.2 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 10)) (10.0.1)\n",
+      "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 11)) (5.9.0)\n",
+      "Requirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 12)) (6.0.1)\n",
+      "Requirement already satisfied: requests>=2.23.0 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 13)) (2.31.0)\n",
+      "Collecting scipy>=1.4.1 (from -r requirements.txt (line 14))\n",
+      "  Downloading scipy-1.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.6/60.6 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hCollecting thop>=0.1.1 (from -r requirements.txt (line 15))\n",
+      "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl.metadata (2.7 kB)\n",
+      "Requirement already satisfied: torch>=1.7.0 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 16)) (2.2.0)\n",
+      "Requirement already satisfied: torchvision>=0.8.1 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 17)) (0.17.0)\n",
+      "Requirement already satisfied: tqdm>=4.64.0 in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 18)) (4.65.0)\n",
+      "Collecting tensorboard>=2.4.1 (from -r requirements.txt (line 22))\n",
+      "  Downloading tensorboard-2.17.0-py3-none-any.whl.metadata (1.6 kB)\n",
+      "Collecting pandas>=1.1.4 (from -r requirements.txt (line 27))\n",
+      "  Downloading pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n",
+      "Collecting seaborn>=0.11.0 (from -r requirements.txt (line 28))\n",
+      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Collecting albumentations>=1.0.3 (from -r requirements.txt (line 46))\n",
+      "  Downloading albumentations-1.4.8-py3-none-any.whl.metadata (37 kB)\n",
+      "Collecting pycocotools>=2.0 (from -r requirements.txt (line 47))\n",
+      "  Downloading pycocotools-2.0.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.1 kB)\n",
+      "Collecting gitdb<5,>=4.0.1 (from gitpython->-r requirements.txt (line 5))\n",
+      "  Downloading gitdb-4.0.11-py3-none-any.whl.metadata (1.2 kB)\n",
+      "Requirement already satisfied: decorator in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (5.1.1)\n",
+      "Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (0.18.1)\n",
+      "Requirement already satisfied: matplotlib-inline in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (0.1.6)\n",
+      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (3.0.43)\n",
+      "Requirement already satisfied: pygments>=2.4.0 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (2.15.1)\n",
+      "Requirement already satisfied: stack-data in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (0.2.0)\n",
+      "Requirement already satisfied: traitlets>=5 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (5.7.1)\n",
+      "Requirement already satisfied: exceptiongroup in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (1.2.0)\n",
+      "Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.10/site-packages (from ipython->-r requirements.txt (line 6)) (4.8.0)\n",
+      "Collecting contourpy>=1.0.1 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB)\n",
+      "Collecting cycler>=0.10 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Collecting fonttools>=4.22.0 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading fonttools-4.53.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (162 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.2/162.2 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hCollecting kiwisolver>=1.3.1 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (6.4 kB)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->-r requirements.txt (line 7)) (23.1)\n",
+      "Collecting pyparsing>=2.3.1 (from matplotlib>=3.2.2->-r requirements.txt (line 7))\n",
+      "  Downloading pyparsing-3.1.2-py3-none-any.whl.metadata (5.1 kB)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.2.2->-r requirements.txt (line 7)) (2.9.0.post0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->-r requirements.txt (line 13)) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->-r requirements.txt (line 13)) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->-r requirements.txt (line 13)) (1.26.18)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.23.0->-r requirements.txt (line 13)) (2023.11.17)\n",
+      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->-r requirements.txt (line 16)) (3.13.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->-r requirements.txt (line 16)) (4.9.0)\n",
+      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->-r requirements.txt (line 16)) (1.12)\n",
+      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->-r requirements.txt (line 16)) (3.1)\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->-r requirements.txt (line 16)) (3.1.2)\n",
+      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.0->-r requirements.txt (line 16)) (2023.12.2)\n",
+      "Collecting absl-py>=0.4 (from tensorboard>=2.4.1->-r requirements.txt (line 22))\n",
+      "  Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\n",
+      "Collecting grpcio>=1.48.2 (from tensorboard>=2.4.1->-r requirements.txt (line 22))\n",
+      "  Downloading grpcio-1.64.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.3 kB)\n",
+      "Collecting markdown>=2.6.8 (from tensorboard>=2.4.1->-r requirements.txt (line 22))\n",
+      "  Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB)\n",
+      "Collecting protobuf!=4.24.0,<5.0.0,>=3.19.6 (from tensorboard>=2.4.1->-r requirements.txt (line 22))\n",
+      "  Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes)\n",
+      "Requirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard>=2.4.1->-r requirements.txt (line 22)) (68.2.2)\n",
+      "Requirement already satisfied: six>1.9 in /opt/conda/lib/python3.10/site-packages (from tensorboard>=2.4.1->-r requirements.txt (line 22)) (1.16.0)\n",
+      "Collecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard>=2.4.1->-r requirements.txt (line 22))\n",
+      "  Downloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl.metadata (1.1 kB)\n",
+      "Collecting werkzeug>=1.0.1 (from tensorboard>=2.4.1->-r requirements.txt (line 22))\n",
+      "  Downloading werkzeug-3.0.3-py3-none-any.whl.metadata (3.7 kB)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.4->-r requirements.txt (line 27)) (2023.3.post1)\n",
+      "Collecting tzdata>=2022.7 (from pandas>=1.1.4->-r requirements.txt (line 27))\n",
+      "  Downloading tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
+      "Collecting scikit-image>=0.21.0 (from albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading scikit_image-0.23.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n",
+      "Collecting scikit-learn>=1.3.2 (from albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading scikit_learn-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
+      "Collecting pydantic>=2.7.0 (from albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading pydantic-2.7.3-py3-none-any.whl.metadata (108 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109.0/109.0 kB\u001b[0m \u001b[31m31.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hCollecting albucore>=0.0.4 (from albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading albucore-0.0.9-py3-none-any.whl.metadata (3.1 kB)\n",
+      "Collecting opencv-python-headless>=4.9.0.80 (from albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading opencv_python_headless-4.10.0.82-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
+      "Requirement already satisfied: tomli>=2.0.1 in /opt/conda/lib/python3.10/site-packages (from albucore>=0.0.4->albumentations>=1.0.3->-r requirements.txt (line 46)) (2.0.1)\n",
+      "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython->-r requirements.txt (line 5))\n",
+      "  Downloading smmap-5.0.1-py3-none-any.whl.metadata (4.3 kB)\n",
+      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/lib/python3.10/site-packages (from jedi>=0.16->ipython->-r requirements.txt (line 6)) (0.8.3)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.10/site-packages (from pexpect>4.3->ipython->-r requirements.txt (line 6)) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/conda/lib/python3.10/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython->-r requirements.txt (line 6)) (0.2.5)\n",
+      "Collecting annotated-types>=0.4.0 (from pydantic>=2.7.0->albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading annotated_types-0.7.0-py3-none-any.whl.metadata (15 kB)\n",
+      "Collecting pydantic-core==2.18.4 (from pydantic>=2.7.0->albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.5 kB)\n",
+      "Collecting imageio>=2.33 (from scikit-image>=0.21.0->albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading imageio-2.34.1-py3-none-any.whl.metadata (4.9 kB)\n",
+      "Collecting tifffile>=2022.8.12 (from scikit-image>=0.21.0->albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading tifffile-2024.5.22-py3-none-any.whl.metadata (30 kB)\n",
+      "Collecting lazy-loader>=0.4 (from scikit-image>=0.21.0->albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading lazy_loader-0.4-py3-none-any.whl.metadata (7.6 kB)\n",
+      "Collecting joblib>=1.2.0 (from scikit-learn>=1.3.2->albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Collecting threadpoolctl>=3.1.0 (from scikit-learn>=1.3.2->albumentations>=1.0.3->-r requirements.txt (line 46))\n",
+      "  Downloading threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard>=2.4.1->-r requirements.txt (line 22)) (2.1.3)\n",
+      "Requirement already satisfied: executing in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython->-r requirements.txt (line 6)) (0.8.3)\n",
+      "Requirement already satisfied: asttokens in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython->-r requirements.txt (line 6)) (2.0.5)\n",
+      "Requirement already satisfied: pure-eval in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython->-r requirements.txt (line 6)) (0.2.2)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.7.0->-r requirements.txt (line 16)) (1.3.0)\n",
+      "Downloading GitPython-3.1.43-py3-none-any.whl (207 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m35.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading matplotlib-3.9.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m127.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hDownloading opencv_python-4.10.0.82-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62.5 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 MB\u001b[0m \u001b[31m89.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading scipy-1.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.6/38.6 MB\u001b[0m \u001b[31m100.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
+      "Downloading tensorboard-2.17.0-py3-none-any.whl (5.5 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m148.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.0 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.0/13.0 MB\u001b[0m \u001b[31m151.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n",
+      "\u001b[?25hDownloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m50.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading albumentations-1.4.8-py3-none-any.whl (156 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m156.8/156.8 kB\u001b[0m \u001b[31m35.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pycocotools-2.0.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (426 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m426.2/426.2 kB\u001b[0m \u001b[31m61.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading absl_py-2.1.0-py3-none-any.whl (133 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.7/133.7 kB\u001b[0m \u001b[31m33.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading albucore-0.0.9-py3-none-any.whl (7.9 kB)\n",
+      "Downloading contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (305 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m305.2/305.2 kB\u001b[0m \u001b[31m50.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
+      "Downloading fonttools-4.53.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.6/4.6 MB\u001b[0m \u001b[31m141.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading grpcio-1.64.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m140.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m115.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading Markdown-3.6-py3-none-any.whl (105 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.4/105.4 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading opencv_python_headless-4.10.0.82-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.9 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.9/49.9 MB\u001b[0m \u001b[31m93.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl (294 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.6/294.6 kB\u001b[0m \u001b[31m49.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pydantic-2.7.3-py3-none-any.whl (409 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m409.6/409.6 kB\u001b[0m \u001b[31m70.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m117.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pyparsing-3.1.2-py3-none-any.whl (103 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.2/103.2 kB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading scikit_image-0.23.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.7 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.7/14.7 MB\u001b[0m \u001b[31m145.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hDownloading scikit_learn-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m137.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hDownloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl (6.6 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.6/6.6 MB\u001b[0m \u001b[31m96.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mta \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hDownloading tzdata-2024.1-py2.py3-none-any.whl (345 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m345.4/345.4 kB\u001b[0m \u001b[31m51.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading werkzeug-3.0.3-py3-none-any.whl (227 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.3/227.3 kB\u001b[0m \u001b[31m42.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading annotated_types-0.7.0-py3-none-any.whl (13 kB)\n",
+      "Downloading imageio-2.34.1-py3-none-any.whl (313 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m313.5/313.5 kB\u001b[0m \u001b[31m56.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading joblib-1.4.2-py3-none-any.whl (301 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.8/301.8 kB\u001b[0m \u001b[31m49.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading lazy_loader-0.4-py3-none-any.whl (12 kB)\n",
+      "Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
+      "Downloading threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n",
+      "Downloading tifffile-2024.5.22-py3-none-any.whl (225 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.5/225.5 kB\u001b[0m \u001b[31m49.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: werkzeug, tzdata, tifffile, threadpoolctl, tensorboard-data-server, smmap, scipy, pyparsing, pydantic-core, protobuf, opencv-python-headless, opencv-python, markdown, lazy-loader, kiwisolver, joblib, imageio, grpcio, fonttools, cycler, contourpy, annotated-types, absl-py, tensorboard, scikit-learn, scikit-image, pydantic, pandas, matplotlib, gitdb, albucore, thop, seaborn, pycocotools, gitpython, albumentations\n",
+      "Successfully installed absl-py-2.1.0 albucore-0.0.9 albumentations-1.4.8 annotated-types-0.7.0 contourpy-1.2.1 cycler-0.12.1 fonttools-4.53.0 gitdb-4.0.11 gitpython-3.1.43 grpcio-1.64.1 imageio-2.34.1 joblib-1.4.2 kiwisolver-1.4.5 lazy-loader-0.4 markdown-3.6 matplotlib-3.9.0 opencv-python-4.10.0.82 opencv-python-headless-4.10.0.82 pandas-2.2.2 protobuf-4.25.3 pycocotools-2.0.7 pydantic-2.7.3 pydantic-core-2.18.4 pyparsing-3.1.2 scikit-image-0.23.2 scikit-learn-1.5.0 scipy-1.13.1 seaborn-0.13.2 smmap-5.0.1 tensorboard-2.17.0 tensorboard-data-server-0.7.2 thop-0.1.1.post2209072238 threadpoolctl-3.5.0 tifffile-2024.5.22 tzdata-2024.1 werkzeug-3.0.3\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -r  requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease\n",
+      "Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease                     \n",
+      "Hit:3 http://security.ubuntu.com/ubuntu jammy-security InRelease\n",
+      "Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n",
+      "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
+      "Reading package lists... Done\n",
+      "Reading package lists... Done\n",
+      "Building dependency tree... Done\n",
+      "Reading state information... Done\n",
+      "The following additional packages will be installed:\n",
+      "  libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1\n",
+      "  libdrm2 libgl1 libgl1-amber-dri libgl1-mesa-dri libglapi-mesa libglvnd0\n",
+      "  libglx-mesa0 libglx0 libllvm15 libpciaccess0 libsensors-config libsensors5\n",
+      "  libx11-6 libx11-data libx11-xcb1 libxau6 libxcb-dri2-0 libxcb-dri3-0\n",
+      "  libxcb-glx0 libxcb-present0 libxcb-randr0 libxcb-shm0 libxcb-sync1\n",
+      "  libxcb-xfixes0 libxcb1 libxdmcp6 libxext6 libxfixes3 libxshmfence1\n",
+      "  libxxf86vm1\n",
+      "Suggested packages:\n",
+      "  pciutils lm-sensors\n",
+      "The following NEW packages will be installed:\n",
+      "  libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1\n",
+      "  libdrm2 libgl1 libgl1-amber-dri libgl1-mesa-dri libgl1-mesa-glx\n",
+      "  libglapi-mesa libglvnd0 libglx-mesa0 libglx0 libllvm15 libpciaccess0\n",
+      "  libsensors-config libsensors5 libx11-6 libx11-data libx11-xcb1 libxau6\n",
+      "  libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 libxcb-randr0\n",
+      "  libxcb-shm0 libxcb-sync1 libxcb-xfixes0 libxcb1 libxdmcp6 libxext6\n",
+      "  libxfixes3 libxshmfence1 libxxf86vm1\n",
+      "0 upgraded, 36 newly installed, 0 to remove and 56 not upgraded.\n",
+      "Need to get 40.1 MB of archives.\n",
+      "After this operation, 173 MB of additional disk space will be used.\n",
+      "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-common all 2.4.113-2~ubuntu0.22.04.1 [5450 B]\n",
+      "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm2 amd64 2.4.113-2~ubuntu0.22.04.1 [38.1 kB]\n",
+      "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxau6 amd64 1:1.0.9-1build5 [7634 B]\n",
+      "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxdmcp6 amd64 1:1.1.3-0ubuntu5 [10.9 kB]\n",
+      "Get:5 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb1 amd64 1.14-3ubuntu3 [49.0 kB]\n",
+      "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libx11-data all 2:1.7.5-1ubuntu0.3 [120 kB]\n",
+      "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libx11-6 amd64 2:1.7.5-1ubuntu0.3 [667 kB]\n",
+      "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxext6 amd64 2:1.3.4-1build1 [31.8 kB]\n",
+      "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-amdgpu1 amd64 2.4.113-2~ubuntu0.22.04.1 [19.9 kB]\n",
+      "Get:10 http://archive.ubuntu.com/ubuntu jammy/main amd64 libpciaccess0 amd64 0.16-3 [19.1 kB]\n",
+      "Get:11 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-intel1 amd64 2.4.113-2~ubuntu0.22.04.1 [66.7 kB]\n",
+      "Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-nouveau2 amd64 2.4.113-2~ubuntu0.22.04.1 [17.5 kB]\n",
+      "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libdrm-radeon1 amd64 2.4.113-2~ubuntu0.22.04.1 [21.6 kB]\n",
+      "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libglapi-mesa amd64 23.2.1-1ubuntu3.1~22.04.2 [37.1 kB]\n",
+      "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgl1-amber-dri amd64 21.3.9-0ubuntu1~22.04.1 [4218 kB]\n",
+      "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libllvm15 amd64 1:15.0.7-0ubuntu0.22.04.3 [25.4 MB]\n",
+      "Get:17 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsensors-config all 1:3.6.0-7ubuntu1 [5274 B]\n",
+      "Get:18 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsensors5 amd64 1:3.6.0-7ubuntu1 [26.3 kB]\n",
+      "Get:19 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-dri3-0 amd64 1.14-3ubuntu3 [6968 B]\n",
+      "Get:20 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgl1-mesa-dri amd64 23.2.1-1ubuntu3.1~22.04.2 [8860 kB]\n",
+      "Get:21 http://archive.ubuntu.com/ubuntu jammy/main amd64 libglvnd0 amd64 1.4.0-1 [73.6 kB]\n",
+      "Get:22 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libx11-xcb1 amd64 2:1.7.5-1ubuntu0.3 [7802 B]\n",
+      "Get:23 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-dri2-0 amd64 1.14-3ubuntu3 [7206 B]\n",
+      "Get:24 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-glx0 amd64 1.14-3ubuntu3 [25.9 kB]\n",
+      "Get:25 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-present0 amd64 1.14-3ubuntu3 [5734 B]\n",
+      "Get:26 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-randr0 amd64 1.14-3ubuntu3 [18.3 kB]\n",
+      "Get:27 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-shm0 amd64 1.14-3ubuntu3 [5780 B]\n",
+      "Get:28 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-sync1 amd64 1.14-3ubuntu3 [9416 B]\n",
+      "Get:29 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-xfixes0 amd64 1.14-3ubuntu3 [9996 B]\n",
+      "Get:30 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxfixes3 amd64 1:6.0.0-1 [11.7 kB]\n",
+      "Get:31 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxshmfence1 amd64 1.3-1build4 [5394 B]\n",
+      "Get:32 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxxf86vm1 amd64 1:1.1.4-1build3 [10.4 kB]\n",
+      "Get:33 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libglx-mesa0 amd64 23.2.1-1ubuntu3.1~22.04.2 [158 kB]\n",
+      "Get:34 http://archive.ubuntu.com/ubuntu jammy/main amd64 libglx0 amd64 1.4.0-1 [41.0 kB]\n",
+      "Get:35 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgl1 amd64 1.4.0-1 [110 kB]\n",
+      "Get:36 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libgl1-mesa-glx amd64 23.0.4-0ubuntu1~22.04.1 [5584 B]\n",
+      "Fetched 40.1 MB in 3s (14.7 MB/s)\n",
+      "debconf: delaying package configuration, since apt-utils is not installed\n",
+      "Selecting previously unselected package libdrm-common.\n",
+      "(Reading database ... 22469 files and directories currently installed.)\n",
+      "Preparing to unpack .../00-libdrm-common_2.4.113-2~ubuntu0.22.04.1_all.deb ...\n",
+      "Unpacking libdrm-common (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libdrm2:amd64.\n",
+      "Preparing to unpack .../01-libdrm2_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm2:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libxau6:amd64.\n",
+      "Preparing to unpack .../02-libxau6_1%3a1.0.9-1build5_amd64.deb ...\n",
+      "Unpacking libxau6:amd64 (1:1.0.9-1build5) ...\n",
+      "Selecting previously unselected package libxdmcp6:amd64.\n",
+      "Preparing to unpack .../03-libxdmcp6_1%3a1.1.3-0ubuntu5_amd64.deb ...\n",
+      "Unpacking libxdmcp6:amd64 (1:1.1.3-0ubuntu5) ...\n",
+      "Selecting previously unselected package libxcb1:amd64.\n",
+      "Preparing to unpack .../04-libxcb1_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb1:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libx11-data.\n",
+      "Preparing to unpack .../05-libx11-data_2%3a1.7.5-1ubuntu0.3_all.deb ...\n",
+      "Unpacking libx11-data (2:1.7.5-1ubuntu0.3) ...\n",
+      "Selecting previously unselected package libx11-6:amd64.\n",
+      "Preparing to unpack .../06-libx11-6_2%3a1.7.5-1ubuntu0.3_amd64.deb ...\n",
+      "Unpacking libx11-6:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
+      "Selecting previously unselected package libxext6:amd64.\n",
+      "Preparing to unpack .../07-libxext6_2%3a1.3.4-1build1_amd64.deb ...\n",
+      "Unpacking libxext6:amd64 (2:1.3.4-1build1) ...\n",
+      "Selecting previously unselected package libdrm-amdgpu1:amd64.\n",
+      "Preparing to unpack .../08-libdrm-amdgpu1_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm-amdgpu1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libpciaccess0:amd64.\n",
+      "Preparing to unpack .../09-libpciaccess0_0.16-3_amd64.deb ...\n",
+      "Unpacking libpciaccess0:amd64 (0.16-3) ...\n",
+      "Selecting previously unselected package libdrm-intel1:amd64.\n",
+      "Preparing to unpack .../10-libdrm-intel1_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm-intel1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libdrm-nouveau2:amd64.\n",
+      "Preparing to unpack .../11-libdrm-nouveau2_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm-nouveau2:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libdrm-radeon1:amd64.\n",
+      "Preparing to unpack .../12-libdrm-radeon1_2.4.113-2~ubuntu0.22.04.1_amd64.deb ...\n",
+      "Unpacking libdrm-radeon1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Selecting previously unselected package libglapi-mesa:amd64.\n",
+      "Preparing to unpack .../13-libglapi-mesa_23.2.1-1ubuntu3.1~22.04.2_amd64.deb ...\n",
+      "Unpacking libglapi-mesa:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Selecting previously unselected package libgl1-amber-dri:amd64.\n",
+      "Preparing to unpack .../14-libgl1-amber-dri_21.3.9-0ubuntu1~22.04.1_amd64.deb ...\n",
+      "Unpacking libgl1-amber-dri:amd64 (21.3.9-0ubuntu1~22.04.1) ...\n",
+      "Selecting previously unselected package libllvm15:amd64.\n",
+      "Preparing to unpack .../15-libllvm15_1%3a15.0.7-0ubuntu0.22.04.3_amd64.deb ...\n",
+      "Unpacking libllvm15:amd64 (1:15.0.7-0ubuntu0.22.04.3) ...\n",
+      "Selecting previously unselected package libsensors-config.\n",
+      "Preparing to unpack .../16-libsensors-config_1%3a3.6.0-7ubuntu1_all.deb ...\n",
+      "Unpacking libsensors-config (1:3.6.0-7ubuntu1) ...\n",
+      "Selecting previously unselected package libsensors5:amd64.\n",
+      "Preparing to unpack .../17-libsensors5_1%3a3.6.0-7ubuntu1_amd64.deb ...\n",
+      "Unpacking libsensors5:amd64 (1:3.6.0-7ubuntu1) ...\n",
+      "Selecting previously unselected package libxcb-dri3-0:amd64.\n",
+      "Preparing to unpack .../18-libxcb-dri3-0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-dri3-0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libgl1-mesa-dri:amd64.\n",
+      "Preparing to unpack .../19-libgl1-mesa-dri_23.2.1-1ubuntu3.1~22.04.2_amd64.deb ...\n",
+      "Unpacking libgl1-mesa-dri:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Selecting previously unselected package libglvnd0:amd64.\n",
+      "Preparing to unpack .../20-libglvnd0_1.4.0-1_amd64.deb ...\n",
+      "Unpacking libglvnd0:amd64 (1.4.0-1) ...\n",
+      "Selecting previously unselected package libx11-xcb1:amd64.\n",
+      "Preparing to unpack .../21-libx11-xcb1_2%3a1.7.5-1ubuntu0.3_amd64.deb ...\n",
+      "Unpacking libx11-xcb1:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
+      "Selecting previously unselected package libxcb-dri2-0:amd64.\n",
+      "Preparing to unpack .../22-libxcb-dri2-0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-dri2-0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-glx0:amd64.\n",
+      "Preparing to unpack .../23-libxcb-glx0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-glx0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-present0:amd64.\n",
+      "Preparing to unpack .../24-libxcb-present0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-present0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-randr0:amd64.\n",
+      "Preparing to unpack .../25-libxcb-randr0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-randr0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-shm0:amd64.\n",
+      "Preparing to unpack .../26-libxcb-shm0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-shm0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-sync1:amd64.\n",
+      "Preparing to unpack .../27-libxcb-sync1_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-sync1:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxcb-xfixes0:amd64.\n",
+      "Preparing to unpack .../28-libxcb-xfixes0_1.14-3ubuntu3_amd64.deb ...\n",
+      "Unpacking libxcb-xfixes0:amd64 (1.14-3ubuntu3) ...\n",
+      "Selecting previously unselected package libxfixes3:amd64.\n",
+      "Preparing to unpack .../29-libxfixes3_1%3a6.0.0-1_amd64.deb ...\n",
+      "Unpacking libxfixes3:amd64 (1:6.0.0-1) ...\n",
+      "Selecting previously unselected package libxshmfence1:amd64.\n",
+      "Preparing to unpack .../30-libxshmfence1_1.3-1build4_amd64.deb ...\n",
+      "Unpacking libxshmfence1:amd64 (1.3-1build4) ...\n",
+      "Selecting previously unselected package libxxf86vm1:amd64.\n",
+      "Preparing to unpack .../31-libxxf86vm1_1%3a1.1.4-1build3_amd64.deb ...\n",
+      "Unpacking libxxf86vm1:amd64 (1:1.1.4-1build3) ...\n",
+      "Selecting previously unselected package libglx-mesa0:amd64.\n",
+      "Preparing to unpack .../32-libglx-mesa0_23.2.1-1ubuntu3.1~22.04.2_amd64.deb ...\n",
+      "Unpacking libglx-mesa0:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Selecting previously unselected package libglx0:amd64.\n",
+      "Preparing to unpack .../33-libglx0_1.4.0-1_amd64.deb ...\n",
+      "Unpacking libglx0:amd64 (1.4.0-1) ...\n",
+      "Selecting previously unselected package libgl1:amd64.\n",
+      "Preparing to unpack .../34-libgl1_1.4.0-1_amd64.deb ...\n",
+      "Unpacking libgl1:amd64 (1.4.0-1) ...\n",
+      "Selecting previously unselected package libgl1-mesa-glx:amd64.\n",
+      "Preparing to unpack .../35-libgl1-mesa-glx_23.0.4-0ubuntu1~22.04.1_amd64.deb ...\n",
+      "Unpacking libgl1-mesa-glx:amd64 (23.0.4-0ubuntu1~22.04.1) ...\n",
+      "Setting up libpciaccess0:amd64 (0.16-3) ...\n",
+      "Setting up libxau6:amd64 (1:1.0.9-1build5) ...\n",
+      "Setting up libxdmcp6:amd64 (1:1.1.3-0ubuntu5) ...\n",
+      "Setting up libxcb1:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libxcb-xfixes0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libglvnd0:amd64 (1.4.0-1) ...\n",
+      "Setting up libxcb-glx0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libsensors-config (1:3.6.0-7ubuntu1) ...\n",
+      "Setting up libxcb-shm0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libxcb-present0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libx11-data (2:1.7.5-1ubuntu0.3) ...\n",
+      "Setting up libxcb-sync1:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libsensors5:amd64 (1:3.6.0-7ubuntu1) ...\n",
+      "Setting up libglapi-mesa:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Setting up libxcb-dri2-0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libxshmfence1:amd64 (1.3-1build4) ...\n",
+      "Setting up libxcb-randr0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libllvm15:amd64 (1:15.0.7-0ubuntu0.22.04.3) ...\n",
+      "Setting up libx11-6:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
+      "Setting up libdrm-common (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libxcb-dri3-0:amd64 (1.14-3ubuntu3) ...\n",
+      "Setting up libx11-xcb1:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
+      "Setting up libxext6:amd64 (2:1.3.4-1build1) ...\n",
+      "Setting up libxxf86vm1:amd64 (1:1.1.4-1build3) ...\n",
+      "Setting up libxfixes3:amd64 (1:6.0.0-1) ...\n",
+      "Setting up libdrm2:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libdrm-amdgpu1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libdrm-nouveau2:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libdrm-radeon1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libdrm-intel1:amd64 (2.4.113-2~ubuntu0.22.04.1) ...\n",
+      "Setting up libgl1-mesa-dri:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Setting up libgl1-amber-dri:amd64 (21.3.9-0ubuntu1~22.04.1) ...\n",
+      "Setting up libglx-mesa0:amd64 (23.2.1-1ubuntu3.1~22.04.2) ...\n",
+      "Setting up libglx0:amd64 (1.4.0-1) ...\n",
+      "Setting up libgl1:amd64 (1.4.0-1) ...\n",
+      "Setting up libgl1-mesa-glx:amd64 (23.0.4-0ubuntu1~22.04.1) ...\n",
+      "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
+      "yes: standard output: Broken pipe\n"
+     ]
+    }
+   ],
+   "source": [
+    "!sudo apt-get update\n",
+    "!yes | sudo apt-get install libgl1-mesa-glx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/workspace/yolov9\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd /workspace/yolov9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting Pillow==9.5.0\n",
+      "  Downloading Pillow-9.5.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (9.5 kB)\n",
+      "Downloading Pillow-9.5.0-cp310-cp310-manylinux_2_28_x86_64.whl (3.4 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m54.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: Pillow\n",
+      "  Attempting uninstall: Pillow\n",
+      "    Found existing installation: Pillow 10.0.1\n",
+      "    Uninstalling Pillow-10.0.1:\n",
+      "      Successfully uninstalled Pillow-10.0.1\n",
+      "Successfully installed Pillow-9.5.0\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install Pillow==9.5.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jjB9EA9yRCyR",
+    "outputId": "2e388fee-6ec5-492d-b698-55d2d1cec2f2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mtrain_dual: \u001b[0mweights=, cfg=/workspace/YOLO9_KMUTT/yolov9/models/detect/yolov9-kmuttX.yaml, data=/workspace/6400 images/data.yaml, hyp=hyp.scratch-high.yaml, epochs=500, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=yolov9-c, exist_ok=False, quad=False, cos_lr=False, flat_cos_lr=False, fixed_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, min_items=0, close_mosaic=15, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
+      "YOLO 🚀 2024-6-7 Python-3.10.13 torch-2.2.0 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)\n",
+      "\n",
+      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, cls_pw=1.0, obj=0.7, obj_pw=1.0, dfl=1.5, iou_t=0.2, anchor_t=5.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.3\n",
+      "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLO 🚀 in ClearML\n",
+      "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLO 🚀 runs in Comet\n",
+      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
+      "Overriding model.yaml nc=80 with nc=10\n",
+      "\n",
+      "                 from  n    params  module                                  arguments                     \n",
+      "  0                -1  1         0  models.common.Silence                   []                            \n",
+      "  1                -1  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n",
+      "  2                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
+      "  3                -1  1    252160  models.common.RepNCSPELAN4              [128, 256, 128, 64, 2]        \n",
+      "  4                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      "  5                -1  1   1004032  models.common.RepNCSPELAN4              [256, 512, 256, 128, 2]       \n",
+      "  6                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      "  7                -1  1   4006912  models.common.RepNCSPELAN4              [512, 1024, 512, 256, 2]      \n",
+      "  8                -1  1   2623488  models.common.ADown                     [1024, 1024]                  \n",
+      "  9                -1  1   4269056  models.common.RepNCSPELAN4              [1024, 1024, 512, 256, 2]     \n",
+      " 10                 1  1      4160  models.common.CBLinear                  [64, [64]]                    \n",
+      " 11                 3  1     49344  models.common.CBLinear                  [256, [64, 128]]              \n",
+      " 12                 5  1    229824  models.common.CBLinear                  [512, [64, 128, 256]]         \n",
+      " 13                 7  1    984000  models.common.CBLinear                  [1024, [64, 128, 256, 512]]   \n",
+      " 14                 9  1   2033600  models.common.CBLinear                  [1024, [64, 128, 256, 512, 1024]]\n",
+      " 15                 0  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n",
+      " 16[10, 11, 12, 13, 14, -1]  1         0  models.common.CBFuse                    [[0, 0, 0, 0, 0]]             \n",
+      " 17                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
+      " 18[11, 12, 13, 14, -1]  1         0  models.common.CBFuse                    [[1, 1, 1, 1]]                \n",
+      " 19                -1  1    252160  models.common.RepNCSPELAN4              [128, 256, 128, 64, 2]        \n",
+      " 20                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      " 21  [12, 13, 14, -1]  1         0  models.common.CBFuse                    [[2, 2, 2]]                   \n",
+      " 22                -1  1   1004032  models.common.RepNCSPELAN4              [256, 512, 256, 128, 2]       \n",
+      " 23                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      " 24      [13, 14, -1]  1         0  models.common.CBFuse                    [[3, 3]]                      \n",
+      " 25                -1  1   4006912  models.common.RepNCSPELAN4              [512, 1024, 512, 256, 2]      \n",
+      " 26                -1  1   2623488  models.common.ADown                     [1024, 1024]                  \n",
+      " 27          [14, -1]  1         0  models.common.CBFuse                    [[4]]                         \n",
+      " 28                -1  1   4269056  models.common.RepNCSPELAN4              [1024, 1024, 512, 256, 2]     \n",
+      " 29                 9  1    787968  models.common.SPPELAN                   [1024, 512, 256]              \n",
+      " 30                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 31           [-1, 7]  1         0  models.common.Concat                    [1]                           \n",
+      " 32                -1  1   4005888  models.common.RepNCSPELAN4              [1536, 512, 512, 256, 2]      \n",
+      " 33                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 34           [-1, 5]  1         0  models.common.Concat                    [1]                           \n",
+      " 35                -1  1   1069056  models.common.RepNCSPELAN4              [1024, 256, 256, 128, 2]      \n",
+      " 36                28  1    787968  models.common.SPPELAN                   [1024, 512, 256]              \n",
+      " 37                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 38          [-1, 25]  1         0  models.common.Concat                    [1]                           \n",
+      " 39                -1  1   4005888  models.common.RepNCSPELAN4              [1536, 512, 512, 256, 2]      \n",
+      " 40                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 41          [-1, 22]  1         0  models.common.Concat                    [1]                           \n",
+      " 42                -1  1   1069056  models.common.RepNCSPELAN4              [1024, 256, 256, 128, 2]      \n",
+      " 43                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      " 44          [-1, 39]  1         0  models.common.Concat                    [1]                           \n",
+      " 45                -1  1   3612672  models.common.RepNCSPELAN4              [768, 512, 512, 256, 2]       \n",
+      " 46                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      " 47          [-1, 36]  1         0  models.common.Concat                    [1]                           \n",
+      " 48                -1  1  13910016  models.common.RepNCSPELAN4              [1024, 1024, 1024, 512, 2]    \n",
+      " 49                -1  1   3948032  models.common.PSA                       [1024, 1024]                  \n",
+      " 50                35  1    249728  models.common.PSA                       [256, 256]                    \n",
+      " 51                32  1    990976  models.common.PSA                       [512, 512]                    \n",
+      " 52                29  1    990976  models.common.PSA                       [512, 512]                    \n",
+      " 53                42  1    249728  models.common.PSA                       [256, 256]                    \n",
+      " 54                45  1    990976  models.common.PSA                       [512, 512]                    \n",
+      " 55[50, 51, 52, 53, 54, 49]  1  12471260  models.yolo.DualDDetect                 [10, [256, 512, 512, 256, 512, 1024]]\n",
+      "yolov9-kmuttX summary: 1643 layers, 79366300 parameters, 79366268 gradients, 263.8 GFLOPs\n",
+      "\n",
+      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
+      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 398 weight(decay=0.0), 417 weight(decay=0.0005), 415 bias\n",
+      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /workspace/6400 images/train/labels.cache... 5120 images, 7 back\u001b[0m\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mScanning /workspace/6400 images/valid/labels.cache... 640 images, 0 backgro\u001b[0m\n",
+      "Plotting labels to runs/train/yolov9-c10/labels.jpg... \n",
+      "Image sizes 640 train, 640 val\n",
+      "Using 4 dataloader workers\n",
+      "Logging results to \u001b[1mruns/train/yolov9-c10\u001b[0m\n",
+      "Starting training for 500 epochs...\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      0/499      16.5G      4.939      7.156      5.232         11        640:  WARNING ⚠️ TensorBoard graph visualization failure Only tensors, lists, tuples of tensors, or dictionary of tensors can be output from traced functions\n",
+      "      0/499      16.7G      5.236      7.764      5.234         23        640:  ^C\n",
+      "      0/499      16.7G      5.236      7.764      5.234         23        640:  \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/train_dual.py\", line 538, in main\n",
+      "    train(opt.hyp, opt, device, callbacks)\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/train_dual.py\", line 314, in train\n",
+      "    pred = model(imgs)  # forward\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/models/yolo.py\", line 633, in forward\n",
+      "    return self._forward_once(x, profile, visualize)  # single-scale inference, train\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/models/yolo.py\", line 533, in _forward_once\n",
+      "    x = m(x)  # run\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/models/common.py\", line 613, in forward\n",
+      "    y.extend((m(y[-1])) for m in [self.cv2, self.cv3])\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/models/common.py\", line 613, in <genexpr>\n",
+      "    y.extend((m(y[-1])) for m in [self.cv2, self.cv3])\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "    input = module(input)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/models/common.py\", line 384, in forward\n",
+      "    return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/YOLO9_KMUTT/yolov9/models/common.py\", line 54, in forward\n",
+      "    return self.act(self.bn(self.conv(x)))\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py\", line 175, in forward\n",
+      "    return F.batch_norm(\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py\", line 2482, in batch_norm\n",
+      "    return torch.batch_norm(\n",
+      "KeyboardInterrupt\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python train_dual.py --workers 8 --device 0 --batch 4 --data '/workspace/6400 images/data.yaml' --img 640 --cfg /workspace/YOLO9_KMUTT/yolov9/models/detect/yolov9-kmuttX.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "VRMBngDDsIok"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2024-06-01 23:55:21,192] torch.distributed.run: [WARNING] \n",
+      "[2024-06-01 23:55:21,192] torch.distributed.run: [WARNING] *****************************************\n",
+      "[2024-06-01 23:55:21,192] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \n",
+      "[2024-06-01 23:55:21,192] torch.distributed.run: [WARNING] *****************************************\n",
+      "\u001b[34m\u001b[1mtrain_dual: \u001b[0mweights=, cfg=models/detect/yolov9-c.yaml, data=/workspace/norway/data.yaml, hyp=hyp.scratch-high.yaml, epochs=500, batch_size=30, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0,1, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=True, workers=8, project=runs/train, name=yolov9-c, exist_ok=False, quad=False, cos_lr=False, flat_cos_lr=False, fixed_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, min_items=0, close_mosaic=15, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 530, in main\n",
+      "    assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'\n",
+      "AssertionError: --batch-size 30 must be multiple of WORLD_SIZE\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 530, in main\n",
+      "    assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'\n",
+      "AssertionError: --batch-size 30 must be multiple of WORLD_SIZE\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 530, in main\n",
+      "    assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'\n",
+      "AssertionError: --batch-size 30 must be multiple of WORLD_SIZE\n",
+      "YOLO 🚀 v0.1-94-gcd88016 Python-3.10.13 torch-2.2.0 CUDA:0 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
+      "                                                    CUDA:1 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
+      "\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 530, in main\n",
+      "    assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'\n",
+      "AssertionError: --batch-size 30 must be multiple of WORLD_SIZE\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 530, in main\n",
+      "    assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'\n",
+      "AssertionError: --batch-size 30 must be multiple of WORLD_SIZE\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 530, in main\n",
+      "    assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'\n",
+      "AssertionError: --batch-size 30 must be multiple of WORLD_SIZE\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 530, in main\n",
+      "    assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'\n",
+      "AssertionError: --batch-size 30 must be multiple of WORLD_SIZE\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 530, in main\n",
+      "    assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'\n",
+      "AssertionError: --batch-size 30 must be multiple of WORLD_SIZE\n",
+      "[2024-06-01 23:55:26,209] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 4360) of binary: /opt/conda/bin/python\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/bin/torchrun\", line 33, in <module>\n",
+      "    sys.exit(load_entry_point('torch==2.2.0', 'console_scripts', 'torchrun')())\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 347, in wrapper\n",
+      "    return f(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/distributed/run.py\", line 812, in main\n",
+      "    run(args)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/distributed/run.py\", line 803, in run\n",
+      "    elastic_launch(\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/distributed/launcher/api.py\", line 135, in __call__\n",
+      "    return launch_agent(self._config, self._entrypoint, list(args))\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/distributed/launcher/api.py\", line 268, in launch_agent\n",
+      "    raise ChildFailedError(\n",
+      "torch.distributed.elastic.multiprocessing.errors.ChildFailedError: \n",
+      "============================================================\n",
+      "train_dual.py FAILED\n",
+      "------------------------------------------------------------\n",
+      "Failures:\n",
+      "[1]:\n",
+      "  time      : 2024-06-01_23:55:26\n",
+      "  host      : de3bed18af84\n",
+      "  rank      : 1 (local_rank: 1)\n",
+      "  exitcode  : 1 (pid: 4361)\n",
+      "  error_file: <N/A>\n",
+      "  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n",
+      "[2]:\n",
+      "  time      : 2024-06-01_23:55:26\n",
+      "  host      : de3bed18af84\n",
+      "  rank      : 2 (local_rank: 2)\n",
+      "  exitcode  : 1 (pid: 4362)\n",
+      "  error_file: <N/A>\n",
+      "  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n",
+      "[3]:\n",
+      "  time      : 2024-06-01_23:55:26\n",
+      "  host      : de3bed18af84\n",
+      "  rank      : 3 (local_rank: 3)\n",
+      "  exitcode  : 1 (pid: 4363)\n",
+      "  error_file: <N/A>\n",
+      "  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n",
+      "[4]:\n",
+      "  time      : 2024-06-01_23:55:26\n",
+      "  host      : de3bed18af84\n",
+      "  rank      : 4 (local_rank: 4)\n",
+      "  exitcode  : 1 (pid: 4364)\n",
+      "  error_file: <N/A>\n",
+      "  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n",
+      "[5]:\n",
+      "  time      : 2024-06-01_23:55:26\n",
+      "  host      : de3bed18af84\n",
+      "  rank      : 5 (local_rank: 5)\n",
+      "  exitcode  : 1 (pid: 4365)\n",
+      "  error_file: <N/A>\n",
+      "  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n",
+      "[6]:\n",
+      "  time      : 2024-06-01_23:55:26\n",
+      "  host      : de3bed18af84\n",
+      "  rank      : 6 (local_rank: 6)\n",
+      "  exitcode  : 1 (pid: 4366)\n",
+      "  error_file: <N/A>\n",
+      "  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n",
+      "[7]:\n",
+      "  time      : 2024-06-01_23:55:26\n",
+      "  host      : de3bed18af84\n",
+      "  rank      : 7 (local_rank: 7)\n",
+      "  exitcode  : 1 (pid: 4367)\n",
+      "  error_file: <N/A>\n",
+      "  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n",
+      "------------------------------------------------------------\n",
+      "Root Cause (first observed failure):\n",
+      "[0]:\n",
+      "  time      : 2024-06-01_23:55:26\n",
+      "  host      : de3bed18af84\n",
+      "  rank      : 0 (local_rank: 0)\n",
+      "  exitcode  : 1 (pid: 4360)\n",
+      "  error_file: <N/A>\n",
+      "  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n",
+      "============================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "!torchrun  --nproc_per_node 2 --master_port 9527 train_dual.py  \\\n",
+    "--workers 8 --device 0,1 --sync-bn --batch 30 --data '/workspace/norway/data.yaml'  \\\n",
+    "--img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml \\\n",
+    "--min-items 0 --epochs 500 --close-mosaic 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2024-06-02 02:12:23,563] torch.distributed.run: [WARNING] \n",
+      "[2024-06-02 02:12:23,563] torch.distributed.run: [WARNING] *****************************************\n",
+      "[2024-06-02 02:12:23,563] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \n",
+      "[2024-06-02 02:12:23,563] torch.distributed.run: [WARNING] *****************************************\n",
+      "\u001b[34m\u001b[1mtrain_dual: \u001b[0mweights=, cfg=models/detect/yolov9-c.yaml, data=/workspace/road damage/data.yaml, hyp=hyp.scratch-high.yaml, epochs=500, batch_size=30, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0,1, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=True, workers=8, project=runs/train, name=yolov9-c, exist_ok=False, quad=False, cos_lr=False, flat_cos_lr=False, fixed_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, min_items=0, close_mosaic=15, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
+      "YOLO 🚀 v0.1-94-gcd88016 Python-3.10.13 torch-2.2.0 CUDA:0 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
+      "                                                    CUDA:1 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
+      "\n",
+      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, cls_pw=1.0, obj=0.7, obj_pw=1.0, dfl=1.5, iou_t=0.2, anchor_t=5.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.3\n",
+      "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLO 🚀 in ClearML\n",
+      "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLO 🚀 runs in Comet\n",
+      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
+      "Overriding model.yaml nc=80 with nc=7\n",
+      "\n",
+      "                 from  n    params  module                                  arguments                     \n",
+      "  0                -1  1         0  models.common.Silence                   []                            \n",
+      "  1                -1  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n",
+      "  2                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
+      "  3                -1  1    291456  models.common.RepNCSPELAN4              [128, 256, 128, 64, 3]        \n",
+      "  4                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      "  5                -1  1   1160448  models.common.RepNCSPELAN4              [256, 512, 256, 128, 3]       \n",
+      "  6                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      "  7                -1  1   4105728  models.common.RepNCSPELAN4              [512, 512, 512, 256, 3]       \n",
+      "  8                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      "  9                -1  1   4105728  models.common.RepNCSPELAN4              [512, 512, 512, 256, 3]       \n",
+      " 10                -1  1    656896  models.common.SPPELAN                   [512, 512, 256]               \n",
+      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 12           [-1, 7]  1         0  models.common.Concat                    [1]                           \n",
+      " 13                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n",
+      " 14                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+      " 15           [-1, 5]  1         0  models.common.Concat                    [1]                           \n",
+      " 16                -1  1    912640  models.common.RepNCSPELAN4              [1024, 256, 256, 128, 1]      \n",
+      " 17                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      " 18          [-1, 13]  1         0  models.common.Concat                    [1]                           \n",
+      " 19                -1  1   2988544  models.common.RepNCSPELAN4              [768, 512, 512, 256, 1]       \n",
+      " 20                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      " 21          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
+      " 22                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n",
+      " 23                 5  1    131328  models.common.CBLinear                  [512, [256]]                  \n",
+      " 24                 7  1    393984  models.common.CBLinear                  [512, [256, 512]]             \n",
+      " 25                 9  1    656640  models.common.CBLinear                  [512, [256, 512, 512]]        \n",
+      " 26                 0  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n",
+      " 27                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
+      " 28                -1  1    212864  models.common.RepNCSPELAN4              [128, 256, 128, 64, 1]        \n",
+      " 29                -1  1    164352  models.common.ADown                     [256, 256]                    \n",
+      " 30  [23, 24, 25, -1]  1         0  models.common.CBFuse                    [[0, 0, 0]]                   \n",
+      " 31                -1  1    847616  models.common.RepNCSPELAN4              [256, 512, 256, 128, 1]       \n",
+      " 32                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      " 33      [24, 25, -1]  1         0  models.common.CBFuse                    [[1, 1]]                      \n",
+      " 34                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n",
+      " 35                -1  1    656384  models.common.ADown                     [512, 512]                    \n",
+      " 36          [25, -1]  1         0  models.common.CBFuse                    [[2]]                         \n",
+      " 37                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n",
+      " 38[31, 34, 37, 16, 19, 22]  1  21556682  models.yolo.DualDDetect                 [7, [512, 512, 512, 256, 512, 512]]\n",
+      "yolov9-c summary: 1170 layers, 53901386 parameters, 53901354 gradients, 252.0 GFLOPs\n",
+      "\n",
+      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
+      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 286 weight(decay=0.0), 303 weight(decay=0.00046875), 301 bias\n",
+      "Using SyncBatchNorm()\n",
+      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /workspace/road damage/train/labels... 4672 images, 345 backgrou\u001b[0m\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /workspace/road damage/train/images/Japan_006916_jpg.rf.5dc73c813f7053289379ab9cf4e81173.jpg: 1 duplicate labels removed\n",
+      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /workspace/road damage/train/labels.cache\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mScanning /workspace/road damage/valid/labels... 450 images, 34 backgrounds,\u001b[0m\n",
+      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /workspace/road damage/valid/labels.cache\n",
+      "Plotting labels to runs/train/yolov9-c4/labels.jpg... \n",
+      "Image sizes 640 train, 640 val\n",
+      "Using 16 dataloader workers\n",
+      "Logging results to \u001b[1mruns/train/yolov9-c4\u001b[0m\n",
+      "Starting training for 500 epochs...\n",
+      "\n",
+      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
+      "      0/499      20.4G        4.8      6.503      5.217         74        640:  Exception in thread Thread-11 (plot_images):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"/opt/conda/lib/python3.10/threading.py\", line 953, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/workspace/yolov9/utils/plots.py\", line 300, in plot_images\n",
+      "    annotator.box_label(box, label, color=color)\n",
+      "  File \"/workspace/yolov9/utils/plots.py\", line 86, in box_label\n",
+      "    w, h = self.font.getsize(label)  # text width, height\n",
+      "AttributeError: 'FreeTypeFont' object has no attribute 'getsize'\n",
+      "      0/499      20.4G      4.903      6.657      5.212         61        640:  Exception in thread Thread-12 (plot_images):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"/opt/conda/lib/python3.10/threading.py\", line 953, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/workspace/yolov9/utils/plots.py\", line 300, in plot_images\n",
+      "    annotator.box_label(box, label, color=color)\n",
+      "  File \"/workspace/yolov9/utils/plots.py\", line 86, in box_label\n",
+      "    w, h = self.font.getsize(label)  # text width, height\n",
+      "AttributeError: 'FreeTypeFont' object has no attribute 'getsize'\n",
+      "      0/499      20.4G      4.703      6.511      5.215         57        640:  Exception in thread Thread-13 (plot_images):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"/opt/conda/lib/python3.10/threading.py\", line 953, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/workspace/yolov9/utils/plots.py\", line 300, in plot_images\n",
+      "    annotator.box_label(box, label, color=color)\n",
+      "  File \"/workspace/yolov9/utils/plots.py\", line 86, in box_label\n",
+      "    w, h = self.font.getsize(label)  # text width, height\n",
+      "AttributeError: 'FreeTypeFont' object has no attribute 'getsize'\n",
+      "      0/499      20.5G      4.674      6.804      5.251         49        640:  ^C\n",
+      "[2024-06-02 02:12:52,085] torch.distributed.elastic.agent.server.api: [WARNING] Received Signals.SIGINT death signal, shutting down workers\n",
+      "[2024-06-02 02:12:52,086] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 11363 closing signal SIGINT\n",
+      "[2024-06-02 02:12:52,086] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 11364 closing signal SIGINT\n",
+      "      0/499      20.5G      4.674      6.804      5.251         49        640:  \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 538, in main\n",
+      "    train(opt.hyp, opt, device, callbacks)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 314, in train\n",
+      "    pred = model(imgs)  # forward\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py\", line 1523, in forward\n",
+      "    else self._run_ddp_forward(*inputs, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py\", line 1359, in _run_ddp_forward\n",
+      "    return self.module(*inputs, **kwargs)  # type: ignore[index]\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/yolov9/models/yolo.py\", line 633, in forward\n",
+      "    return self._forward_once(x, profile, visualize)  # single-scale inference, train\n",
+      "  File \"/workspace/yolov9/models/yolo.py\", line 533, in _forward_once\n",
+      "    x = m(x)  # run\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/yolov9/models/yolo.py\", line 225, in forward\n",
+      "    d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "    input = module(input)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/yolov9/models/common.py\", line 54, in forward\n",
+      "    return self.act(self.bn(self.conv(x)))\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py\", line 767, in forward\n",
+      "    return sync_batch_norm.apply(\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/autograd/function.py\", line 553, in apply\n",
+      "    return super().apply(*args, **kwargs)  # type: ignore[misc]\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/_functions.py\", line 89, in forward\n",
+      "    counts = count_all.view(-1)\n",
+      "KeyboardInterrupt\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 644, in <module>\n",
+      "    main(opt)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 538, in main\n",
+      "    train(opt.hyp, opt, device, callbacks)\n",
+      "  File \"/workspace/yolov9/train_dual.py\", line 314, in train\n",
+      "    pred = model(imgs)  # forward\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py\", line 1523, in forward\n",
+      "    else self._run_ddp_forward(*inputs, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/distributed.py\", line 1359, in _run_ddp_forward\n",
+      "    return self.module(*inputs, **kwargs)  # type: ignore[index]\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/yolov9/models/yolo.py\", line 633, in forward\n",
+      "    return self._forward_once(x, profile, visualize)  # single-scale inference, train\n",
+      "  File \"/workspace/yolov9/models/yolo.py\", line 533, in _forward_once\n",
+      "    x = m(x)  # run\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/yolov9/models/yolo.py\", line 225, in forward\n",
+      "    d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "    input = module(input)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/workspace/yolov9/models/common.py\", line 54, in forward\n",
+      "    return self.act(self.bn(self.conv(x)))\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
+      "    return self._call_impl(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py\", line 767, in forward\n",
+      "    return sync_batch_norm.apply(\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/autograd/function.py\", line 553, in apply\n",
+      "    return super().apply(*args, **kwargs)  # type: ignore[misc]\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/_functions.py\", line 89, in forward\n",
+      "    counts = count_all.view(-1)\n",
+      "KeyboardInterrupt\n"
+     ]
+    }
+   ],
+   "source": [
+    "!torchrun  --nproc_per_node 2 --master_port 9527 train_dual.py  \\\n",
+    "--workers 8 --device 0,1 --sync-bn --batch 30 --data '/workspace/road damage/data.yaml'  \\\n",
+    "--img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml \\\n",
+    "--min-items 0 --epochs 500 --close-mosaic 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!torchrun  --nproc_per_node 2 --master_port 9527 train_dual.py  \\\n",
+    "--workers 8 --device 0,1 --sync-bn --batch 30 --data '/workspace/road damage/data.yaml'  \\\n",
+    "--img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml \\\n",
+    "--min-items 0 --epochs 500 --close-mosaic 15"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "L4",
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

app.py

@@ -0,0 +1,190 @@
+import os
+import cv2
+import numpy as np
+import streamlit as st
+import matplotlib.pyplot as plt
+from shapely.geometry import Polygon, box as shapely_box
+import subprocess
+import gradio as gr
+# ... (previous functions remain unchanged)
+
+
+def extract_class_0_coordinates(filename):
+    class_0_coordinates = []
+    current_class = None
+    
+    with open(filename, 'r') as file:
+          
+        for line in file:
+            parts = line.strip().split()
+            if len(parts) == 0:
+                continue
+            
+            if parts[0] == '0':
+                coordinates = [float(x) for x in parts[1:]]
+                class_0_coordinates.extend(coordinates)
+    
+    return class_0_coordinates
+
+def run_yolo_models1(img):
+    # Run YOLOv9 segmentation
+    os.system(f"python segment/predict.py --source {img} --img 640 --device cpu --weights models/segment/best-2.pt --name yolov9_c_640_detect --exist-ok --save-txt")
+
+    # Run YOLOv9 detection
+    os.system(f"python detect.py --source {img} --img 640 --device cpu --weights models/detect/yolov9-s-converted.pt --name yolov9_c_640_detect --exist-ok --save-txt")
+
+def parse_yolo_box(box_string):
+    """Parse a YOLO format bounding box string."""
+    values = list(map(float, box_string.split()))
+    if len(values) < 5:
+        raise ValueError(f"Expected at least 5 values, got {len(values)}")
+    return values[0], values[1], values[2], values[3], values[4]
+
+
+def read_yolo_boxes(file_path):
+    boxes = []
+    with open(file_path, 'r') as f:
+        for line in f:
+            parts = line.strip().split()
+            class_name = COCO_CLASSES[int(parts[0])]
+            x, y, w, h = map(float, parts[1:5])
+            boxes.append((class_name, x, y, w, h))
+    return boxes
+
+def yolo_to_pixel_coord(x, y, img_width, img_height):
+    """Convert a single YOLO coordinate to pixel coordinate."""
+    return int(x * img_width), int(y * img_height)
+
+
+def yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height):
+    """Convert YOLO format coordinates to pixel coordinates."""
+    x1 = int((x_center - width / 2) * img_width)
+    y1 = int((y_center - height / 2) * img_height)
+    x2 = int((x_center + width / 2) * img_width)
+    y2 = int((y_center + height / 2) * img_height)
+    return x1, y1, x2, y2
+
+def box_segment_relationship(yolo_box, segment, img_width, img_height, threshold):
+    """Check the relationship between a bounding box and a segmented area."""
+    class_id, x_center, y_center, width, height = yolo_box
+    x1, y1, x2, y2 = yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height)
+    pixel_segment = convert_segment_to_pixel(segment, img_width, img_height)
+    segment_polygon = Polygon(zip(pixel_segment[::2], pixel_segment[1::2]))
+    box_polygon = shapely_box(x1, y1, x2, y2)
+    
+    if box_polygon.intersects(segment_polygon):
+        return "intersecting"
+    elif box_polygon.distance(segment_polygon) <= threshold:
+        return "obstructed"
+    else:
+        return "not touching"
+def convert_segment_to_pixel(segment, img_width, img_height):
+    """Convert segment coordinates from YOLO format to pixel coordinates."""
+    pixel_segment = []
+    for i in range(0, len(segment), 2):
+        x, y = yolo_to_pixel_coord(segment[i], segment[i+1], img_width, img_height)
+        pixel_segment.extend([x, y])
+    return pixel_segment
+
+def plot_boxes_and_segment(image, yolo_boxes, segment, img_width, img_height, threshold):
+    """Plot the image with intersecting boxes, obstructed boxes, and segment."""
+    fig, ax = plt.subplots(figsize=(12, 8))
+    ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    
+    pixel_segment = convert_segment_to_pixel(segment, img_width, img_height)
+    ax.plot(pixel_segment[::2] + [pixel_segment[0]], pixel_segment[1::2] + [pixel_segment[1]], 'g-', linewidth=2, label='Rail Zone')
+    
+    colors = {'intersecting': 'r', 'obstructed': 'y', 'not touching': 'b'}
+    labels = {'intersecting': 'Intersecting Box', 'obstructed': 'Obstructed Box', 'not touching': 'Non-interacting Box'}
+    
+    for yolo_box in yolo_boxes:
+        class_id, x_center, y_center, width, height = yolo_box
+        x1, y1, x2, y2 = yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height)
+        relationship = box_segment_relationship(yolo_box, segment, img_width, img_height, threshold)
+        color = colors[relationship]
+        label = labels[relationship]
+        ax.add_patch(plt.Rectangle((x1, y1), x2-x1, y2-y1, fill=False, edgecolor=color, linewidth=2, label=label))
+    
+    ax.legend()
+    ax.axis('off')
+    plt.tight_layout()
+    return fig
+
+COCO_CLASSES = [
+    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+    'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+    'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+    'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+    'hair drier', 'toothbrush'
+]
+
+def read_yolo_boxes(file_path):
+    boxes = []
+    with open(file_path, 'r') as f:
+        for line in f:
+            parts = line.strip().split()
+            class_name = COCO_CLASSES[int(parts[0])]
+            x, y, w, h = map(float, parts[1:5])
+            boxes.append((class_name, x, y, w, h))
+    return boxes
+
+import gradio as gr
+import cv2
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+
+
+def analyze_image(image):
+    # Convert PIL Image to numpy array
+    image = np.array(image)
+    
+    # Convert RGB to BGR (OpenCV format)
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    
+    img_height, img_width = image.shape[:2]
+    
+    # Save the uploaded image temporarily
+    temp_image_path = "temp_image.jpg"
+    cv2.imwrite(temp_image_path, image_bgr)
+    
+    # Run YOLO models
+    run_yolo_models1(temp_image_path)
+    label_path = 'runs/predict-seg/yolov9_c_640_detect/labels/temp_image.txt'
+    label_path2 = 'runs/detect/yolov9_c_640_detect/labels/temp_image.txt'
+    
+    segment = extract_class_0_coordinates(label_path)
+    yolo_boxes = read_yolo_boxes(label_path2)
+    threshold = 10  # Set threshold (in pixels)
+    
+    fig = plot_boxes_and_segment(image, yolo_boxes, segment, img_width, img_height, threshold)
+    
+    results = []
+    for class_name, x, y, w, h in yolo_boxes:
+        result = box_segment_relationship((0, x, y, w, h), segment, img_width, img_height, threshold)
+        results.append(f"{class_name} at ({x:.2f}, {y:.2f}) is {result} the segment.")
+    
+    # Clean up temporary files
+    os.remove(temp_image_path)
+    os.remove(label_path)
+    os.remove(label_path2)
+    
+    return fig, "\n".join(results)
+
+iface = gr.Interface(
+    fn=analyze_image,
+    inputs=gr.Image(),
+    outputs=[
+        gr.Plot(label="Analysis Visualization"),
+        gr.Textbox(label="Analysis Results")
+    ],
+    title="Train obstruction detection",
+    description="Upload an image to run analysis."
+)
+
+if __name__ == "__main__":
+    iface.launch()

appt.py

@@ -0,0 +1,148 @@
+import os
+import cv2
+import numpy as np
+import streamlit as st
+import matplotlib.pyplot as plt
+from shapely.geometry import Polygon, box as shapely_box
+import subprocess
+
+# ... (previous functions remain unchanged)
+
+
+def extract_class_0_coordinates(filename):
+    class_0_coordinates = []
+    current_class = None
+    
+    with open(filename, 'r') as file:
+          
+        for line in file:
+            parts = line.strip().split()
+            if len(parts) == 0:
+                continue
+            
+            if parts[0] == '0':
+                coordinates = [float(x) for x in parts[1:]]
+                class_0_coordinates.extend(coordinates)
+    
+    return class_0_coordinates
+
+def run_yolo_models1(img):
+    # Run YOLOv9 segmentation
+    os.system(f"python segment/predict.py --source {img} --img 640 --device cpu --weights models/segment/best-2.pt --name yolov9_c_640_detect --exist-ok --save-txt")
+
+    # Run YOLOv9 detection
+    os.system(f"python detect.py --source {img} --img 640 --device cpu --weights models/detect/yolov9-s-converted.pt --name yolov9_c_640_detect --exist-ok --save-txt")
+
+def parse_yolo_box(box_string):
+    """Parse a YOLO format bounding box string."""
+    values = list(map(float, box_string.split()))
+    if len(values) < 5:
+        raise ValueError(f"Expected at least 5 values, got {len(values)}")
+    return values[0], values[1], values[2], values[3], values[4]
+
+def read_yolo_boxes(file_path):
+    """Read YOLO format bounding boxes from a file."""
+    with open(file_path, 'r') as f:
+        return [parse_yolo_box(line.strip()) for line in f if line.strip()]
+
+def yolo_to_pixel_coord(x, y, img_width, img_height):
+    """Convert a single YOLO coordinate to pixel coordinate."""
+    return int(x * img_width), int(y * img_height)
+
+
+def yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height):
+    """Convert YOLO format coordinates to pixel coordinates."""
+    x1 = int((x_center - width / 2) * img_width)
+    y1 = int((y_center - height / 2) * img_height)
+    x2 = int((x_center + width / 2) * img_width)
+    y2 = int((y_center + height / 2) * img_height)
+    return x1, y1, x2, y2
+
+def box_segment_relationship(yolo_box, segment, img_width, img_height, threshold):
+    """Check the relationship between a bounding box and a segmented area."""
+    class_id, x_center, y_center, width, height = yolo_box
+    x1, y1, x2, y2 = yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height)
+    pixel_segment = convert_segment_to_pixel(segment, img_width, img_height)
+    segment_polygon = Polygon(zip(pixel_segment[::2], pixel_segment[1::2]))
+    box_polygon = shapely_box(x1, y1, x2, y2)
+    
+    if box_polygon.intersects(segment_polygon):
+        return "intersecting"
+    elif box_polygon.distance(segment_polygon) <= threshold:
+        return "obstructed"
+    else:
+        return "not touching"
+def convert_segment_to_pixel(segment, img_width, img_height):
+    """Convert segment coordinates from YOLO format to pixel coordinates."""
+    pixel_segment = []
+    for i in range(0, len(segment), 2):
+        x, y = yolo_to_pixel_coord(segment[i], segment[i+1], img_width, img_height)
+        pixel_segment.extend([x, y])
+    return pixel_segment
+
+def plot_boxes_and_segment(image, yolo_boxes, segment, img_width, img_height, threshold):
+    """Plot the image with intersecting boxes, obstructed boxes, and segment."""
+    fig, ax = plt.subplots(figsize=(12, 8))
+    ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    
+    pixel_segment = convert_segment_to_pixel(segment, img_width, img_height)
+    ax.plot(pixel_segment[::2] + [pixel_segment[0]], pixel_segment[1::2] + [pixel_segment[1]], 'g-', linewidth=2, label='Rail Zone')
+    
+    colors = {'intersecting': 'r', 'obstructed': 'y', 'not touching': 'b'}
+    labels = {'intersecting': 'Intersecting Box', 'obstructed': 'Obstructed Box', 'not touching': 'Non-interacting Box'}
+    
+    for yolo_box in yolo_boxes:
+        class_id, x_center, y_center, width, height = yolo_box
+        x1, y1, x2, y2 = yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height)
+        relationship = box_segment_relationship(yolo_box, segment, img_width, img_height, threshold)
+        color = colors[relationship]
+        label = labels[relationship]
+        ax.add_patch(plt.Rectangle((x1, y1), x2-x1, y2-y1, fill=False, edgecolor=color, linewidth=2, label=label))
+    
+    ax.legend()
+    ax.axis('off')
+    plt.tight_layout()
+    return fig
+
+def main():
+    st.title("YOLO Analysis App")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])
+    if uploaded_file is not None:
+        image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        if st.button('Run Analysis'):
+            with st.spinner("Running detection..."):
+                img_height, img_width = image.shape[:2]
+
+                # Save the uploaded image temporarily
+                temp_image_path = "temp_image.jpg"
+                cv2.imwrite(temp_image_path, image)
+
+                # Run YOLO models
+                run_yolo_models1(temp_image_path)
+
+                label_path = 'runs/predict-seg/yolov9_c_640_detect/labels/temp_image.txt'
+                label_path2 = 'runs/detect/yolov9_c_640_detect/labels/temp_image.txt'
+
+                segment = extract_class_0_coordinates(label_path)
+                yolo_boxes = read_yolo_boxes(label_path2)
+
+                threshold = 10  # Set threshold (in pixels)
+
+                fig = plot_boxes_and_segment(image, yolo_boxes, segment, img_width, img_height, threshold)
+                st.pyplot(fig)
+
+                st.subheader("Analysis Results:")
+                for yolo_box in yolo_boxes:
+                    result = box_segment_relationship(yolo_box, segment, img_width, img_height, threshold)
+                    st.write(f"Box {yolo_box} is {result} the segment.")
+
+                # Clean up temporary files
+                os.remove(temp_image_path)
+                os.remove(label_path)
+                os.remove(label_path2)
+
+if __name__ == "__main__":
+    main()

appt3.py

@@ -0,0 +1,178 @@
+import os
+import cv2
+import numpy as np
+import streamlit as st
+import matplotlib.pyplot as plt
+from shapely.geometry import Polygon, box as shapely_box
+import subprocess
+
+# ... (previous functions remain unchanged)
+
+
+def extract_class_0_coordinates(filename):
+    class_0_coordinates = []
+    current_class = None
+    
+    with open(filename, 'r') as file:
+          
+        for line in file:
+            parts = line.strip().split()
+            if len(parts) == 0:
+                continue
+            
+            if parts[0] == '0':
+                coordinates = [float(x) for x in parts[1:]]
+                class_0_coordinates.extend(coordinates)
+    
+    return class_0_coordinates
+
+def run_yolo_models1(img):
+    # Run YOLOv9 segmentation
+    os.system(f"python segment/predict.py --source {img} --img 640 --device cpu --weights models/segment/best-2.pt --name yolov9_c_640_detect --exist-ok --save-txt")
+
+    # Run YOLOv9 detection
+    os.system(f"python detect.py --source {img} --img 640 --device cpu --weights models/detect/yolov9-s-converted.pt --name yolov9_c_640_detect --exist-ok --save-txt")
+
+def parse_yolo_box(box_string):
+    """Parse a YOLO format bounding box string."""
+    values = list(map(float, box_string.split()))
+    if len(values) < 5:
+        raise ValueError(f"Expected at least 5 values, got {len(values)}")
+    return values[0], values[1], values[2], values[3], values[4]
+
+
+def read_yolo_boxes(file_path):
+    boxes = []
+    with open(file_path, 'r') as f:
+        for line in f:
+            parts = line.strip().split()
+            class_name = COCO_CLASSES[int(parts[0])]
+            x, y, w, h = map(float, parts[1:5])
+            boxes.append((class_name, x, y, w, h))
+    return boxes
+
+def yolo_to_pixel_coord(x, y, img_width, img_height):
+    """Convert a single YOLO coordinate to pixel coordinate."""
+    return int(x * img_width), int(y * img_height)
+
+
+def yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height):
+    """Convert YOLO format coordinates to pixel coordinates."""
+    x1 = int((x_center - width / 2) * img_width)
+    y1 = int((y_center - height / 2) * img_height)
+    x2 = int((x_center + width / 2) * img_width)
+    y2 = int((y_center + height / 2) * img_height)
+    return x1, y1, x2, y2
+
+def box_segment_relationship(yolo_box, segment, img_width, img_height, threshold):
+    """Check the relationship between a bounding box and a segmented area."""
+    class_id, x_center, y_center, width, height = yolo_box
+    x1, y1, x2, y2 = yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height)
+    pixel_segment = convert_segment_to_pixel(segment, img_width, img_height)
+    segment_polygon = Polygon(zip(pixel_segment[::2], pixel_segment[1::2]))
+    box_polygon = shapely_box(x1, y1, x2, y2)
+    
+    if box_polygon.intersects(segment_polygon):
+        return "intersecting"
+    elif box_polygon.distance(segment_polygon) <= threshold:
+        return "obstructed"
+    else:
+        return "not touching"
+def convert_segment_to_pixel(segment, img_width, img_height):
+    """Convert segment coordinates from YOLO format to pixel coordinates."""
+    pixel_segment = []
+    for i in range(0, len(segment), 2):
+        x, y = yolo_to_pixel_coord(segment[i], segment[i+1], img_width, img_height)
+        pixel_segment.extend([x, y])
+    return pixel_segment
+
+def plot_boxes_and_segment(image, yolo_boxes, segment, img_width, img_height, threshold):
+    """Plot the image with intersecting boxes, obstructed boxes, and segment."""
+    fig, ax = plt.subplots(figsize=(12, 8))
+    ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    
+    pixel_segment = convert_segment_to_pixel(segment, img_width, img_height)
+    ax.plot(pixel_segment[::2] + [pixel_segment[0]], pixel_segment[1::2] + [pixel_segment[1]], 'g-', linewidth=2, label='Rail Zone')
+    
+    colors = {'intersecting': 'r', 'obstructed': 'y', 'not touching': 'b'}
+    labels = {'intersecting': 'Intersecting Box', 'obstructed': 'Obstructed Box', 'not touching': 'Non-interacting Box'}
+    
+    for yolo_box in yolo_boxes:
+        class_id, x_center, y_center, width, height = yolo_box
+        x1, y1, x2, y2 = yolo_to_pixel_coords(x_center, y_center, width, height, img_width, img_height)
+        relationship = box_segment_relationship(yolo_box, segment, img_width, img_height, threshold)
+        color = colors[relationship]
+        label = labels[relationship]
+        ax.add_patch(plt.Rectangle((x1, y1), x2-x1, y2-y1, fill=False, edgecolor=color, linewidth=2, label=label))
+    
+    ax.legend()
+    ax.axis('off')
+    plt.tight_layout()
+    return fig
+
+COCO_CLASSES = [
+    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+    'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+    'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+    'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+    'hair drier', 'toothbrush'
+]
+
+def read_yolo_boxes(file_path):
+    boxes = []
+    with open(file_path, 'r') as f:
+        for line in f:
+            parts = line.strip().split()
+            class_name = COCO_CLASSES[int(parts[0])]
+            x, y, w, h = map(float, parts[1:5])
+            boxes.append((class_name, x, y, w, h))
+    return boxes
+
+def main():
+    st.title("YOLO Analysis App")
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])
+    
+    if uploaded_file is not None:
+        # Read the image as BGR
+        image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR)
+        # Convert BGR to RGB
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        
+        st.image(image_rgb, caption='Uploaded Image', use_column_width=True)
+        
+        if st.button('Run Analysis'):
+            with st.spinner("Running detection..."):
+                img_height, img_width = image.shape[:2]
+                
+                # Save the uploaded image temporarily
+                temp_image_path = "temp_image.jpg"
+                cv2.imwrite(temp_image_path, image)
+                
+                # Run YOLO models
+                run_yolo_models1(temp_image_path)
+                label_path = 'runs/predict-seg/yolov9_c_640_detect/labels/temp_image.txt'
+                label_path2 = 'runs/detect/yolov9_c_640_detect/labels/temp_image.txt'
+                
+                segment = extract_class_0_coordinates(label_path)
+                yolo_boxes = read_yolo_boxes(label_path2)
+                
+                threshold = 10  # Set threshold (in pixels)
+                fig = plot_boxes_and_segment(image_rgb, yolo_boxes, segment, img_width, img_height, threshold)
+                st.pyplot(fig)
+                
+                st.subheader("Analysis Results:")
+                for class_name, x, y, w, h in yolo_boxes:
+                    result = box_segment_relationship((0, x, y, w, h), segment, img_width, img_height, threshold)
+                    st.write(f"{class_name} at ({x:.2f}, {y:.2f}) is {result} the segment.")
+                
+                # Clean up temporary files
+                os.remove(temp_image_path)
+                os.remove(label_path)
+                os.remove(label_path2)
+
+if __name__ == "__main__":
+    main()

benchmarks.py

@@ -0,0 +1,142 @@
+import argparse
+import platform
+import sys
+import time
+from pathlib import Path
+
+import pandas as pd
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLO root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+# ROOT = ROOT.relative_to(Path.cwd())  # relative
+
+import export
+from models.experimental import attempt_load
+from models.yolo import SegmentationModel
+from segment.val import run as val_seg
+from utils import notebook_init
+from utils.general import LOGGER, check_yaml, file_size, print_args
+from utils.torch_utils import select_device
+from val import run as val_det
+
+
+def run(
+        weights=ROOT / 'yolo.pt',  # weights path
+        imgsz=640,  # inference size (pixels)
+        batch_size=1,  # batch size
+        data=ROOT / 'data/coco.yaml',  # dataset.yaml path
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        half=False,  # use FP16 half-precision inference
+        test=False,  # test exports only
+        pt_only=False,  # test PyTorch only
+        hard_fail=False,  # throw error on benchmark failure
+):
+    y, t = [], time.time()
+    device = select_device(device)
+    model_type = type(attempt_load(weights, fuse=False))  # DetectionModel, SegmentationModel, etc.
+    for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, CPU, GPU)
+        try:
+            assert i not in (9, 10), 'inference not supported'  # Edge TPU and TF.js are unsupported
+            assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13'  # CoreML
+            if 'cpu' in device.type:
+                assert cpu, 'inference not supported on CPU'
+            if 'cuda' in device.type:
+                assert gpu, 'inference not supported on GPU'
+
+            # Export
+            if f == '-':
+                w = weights  # PyTorch format
+            else:
+                w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]  # all others
+            assert suffix in str(w), 'export failed'
+
+            # Validate
+            if model_type == SegmentationModel:
+                result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
+                metric = result[0][7]  # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
+            else:  # DetectionModel:
+                result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
+                metric = result[0][3]  # (p, r, map50, map, *loss(box, obj, cls))
+            speed = result[2][1]  # times (preprocess, inference, postprocess)
+            y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)])  # MB, mAP, t_inference
+        except Exception as e:
+            if hard_fail:
+                assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}'
+            LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}')
+            y.append([name, None, None, None])  # mAP, t_inference
+        if pt_only and i == 0:
+            break  # break after PyTorch
+
+    # Print results
+    LOGGER.info('\n')
+    parse_opt()
+    notebook_init()  # print system info
+    c = ['Format', 'Size (MB)', 'mAP50-95', 'Inference time (ms)'] if map else ['Format', 'Export', '', '']
+    py = pd.DataFrame(y, columns=c)
+    LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)')
+    LOGGER.info(str(py if map else py.iloc[:, :2]))
+    if hard_fail and isinstance(hard_fail, str):
+        metrics = py['mAP50-95'].array  # values to compare to floor
+        floor = eval(hard_fail)  # minimum metric floor to pass
+        assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: mAP50-95 < floor {floor}'
+    return py
+
+
+def test(
+        weights=ROOT / 'yolo.pt',  # weights path
+        imgsz=640,  # inference size (pixels)
+        batch_size=1,  # batch size
+        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        half=False,  # use FP16 half-precision inference
+        test=False,  # test exports only
+        pt_only=False,  # test PyTorch only
+        hard_fail=False,  # throw error on benchmark failure
+):
+    y, t = [], time.time()
+    device = select_device(device)
+    for i, (name, f, suffix, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, gpu-capable)
+        try:
+            w = weights if f == '-' else \
+                export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]  # weights
+            assert suffix in str(w), 'export failed'
+            y.append([name, True])
+        except Exception:
+            y.append([name, False])  # mAP, t_inference
+
+    # Print results
+    LOGGER.info('\n')
+    parse_opt()
+    notebook_init()  # print system info
+    py = pd.DataFrame(y, columns=['Format', 'Export'])
+    LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)')
+    LOGGER.info(str(py))
+    return py
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', type=str, default=ROOT / 'yolo.pt', help='weights path')
+    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--test', action='store_true', help='test exports only')
+    parser.add_argument('--pt-only', action='store_true', help='test PyTorch only')
+    parser.add_argument('--hard-fail', nargs='?', const=True, default=False, help='Exception on error or < min metric')
+    opt = parser.parse_args()
+    opt.data = check_yaml(opt.data)  # check YAML
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    test(**vars(opt)) if opt.test else run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

classify/predict.py

@@ -0,0 +1,224 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python classify/predict.py --weights yolov5s-cls.pt --source 0                               # webcam
+                                                                   img.jpg                         # image
+                                                                   vid.mp4                         # video
+                                                                   screen                          # screenshot
+                                                                   path/                           # directory
+                                                                   'path/*.jpg'                    # glob
+                                                                   'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                                   'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
+                                           yolov5s-cls.torchscript        # TorchScript
+                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                           yolov5s-cls_openvino_model     # OpenVINO
+                                           yolov5s-cls.engine             # TensorRT
+                                           yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                           yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                           yolov5s-cls.pb                 # TensorFlow GraphDef
+                                           yolov5s-cls.tflite             # TensorFlow Lite
+                                           yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
+                                           yolov5s-cls_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.augmentations import classify_transforms
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
+from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+                           increment_path, print_args, strip_optimizer)
+from utils.plots import Annotator
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+        weights=ROOT / 'yolov5s-cls.pt',  # model.pt path(s)
+        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
+        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
+        imgsz=(224, 224),  # inference size (height, width)
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        view_img=False,  # show results
+        save_txt=False,  # save results to *.txt
+        nosave=False,  # do not save images/videos
+        augment=False,  # augmented inference
+        visualize=False,  # visualize features
+        update=False,  # update all models
+        project=ROOT / 'runs/predict-cls',  # save results to project/name
+        name='exp',  # save results to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        half=False,  # use FP16 half-precision inference
+        dnn=False,  # use OpenCV DNN for ONNX inference
+        vid_stride=1,  # video frame-rate stride
+):
+    source = str(source)
+    save_img = not nosave and not source.endswith('.txt')  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
+    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
+    screenshot = source.lower().startswith('screen')
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
+    else:
+        dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.Tensor(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            results = model(im)
+
+        # Post-process
+        with dt[2]:
+            pred = F.softmax(results, dim=1)  # probabilities
+
+        # Process predictions
+        for i, prob in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f'{i}: '
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
+
+            s += '%gx%g ' % im.shape[2:]  # print string
+            annotator = Annotator(im0, example=str(names), pil=True)
+
+            # Print results
+            top5i = prob.argsort(0, descending=True)[:5].tolist()  # top 5 indices
+            s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
+
+            # Write results
+            text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i)
+            if save_img or view_img:  # Add bbox to image
+                annotator.text((32, 32), text, txt_color=(255, 255, 255))
+            if save_txt:  # Write to file
+                with open(f'{txt_path}.txt', 'a') as f:
+                    f.write(text + '\n')
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == 'Linux' and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == 'image':
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[i].release()  # release previous video writer
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
+
+    # Print results
+    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
+    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)')
+    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
+    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--view-img', action='store_true', help='show results')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
+    parser.add_argument('--augment', action='store_true', help='augmented inference')
+    parser.add_argument('--visualize', action='store_true', help='visualize features')
+    parser.add_argument('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default=ROOT / 'runs/predict-cls', help='save results to project/name')
+    parser.add_argument('--name', default='exp', help='save results to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(exclude=('tensorboard', 'thop'))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

classify/train.py

@@ -0,0 +1,333 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Train a YOLOv5 classifier model on a classification dataset
+
+Usage - Single-GPU training:
+    $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
+
+Usage - Multi-GPU DDP training:
+    $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+
+Datasets:           --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
+YOLOv5-cls models:  --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
+Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+from copy import deepcopy
+from datetime import datetime
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import torch.hub as hub
+import torch.optim.lr_scheduler as lr_scheduler
+import torchvision
+from torch.cuda import amp
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from classify import val as validate
+from models.experimental import attempt_load
+from models.yolo import ClassificationModel, DetectionModel
+from utils.dataloaders import create_classification_dataloader
+from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_info, check_git_status,
+                           check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save)
+from utils.loggers import GenericLogger
+from utils.plots import imshow_cls
+from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
+                               smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first)
+
+LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv('RANK', -1))
+WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
+GIT_INFO = check_git_info()
+
+
+def train(opt, device):
+    init_seeds(opt.seed + 1 + RANK, deterministic=True)
+    save_dir, data, bs, epochs, nw, imgsz, pretrained = \
+        opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), \
+        opt.imgsz, str(opt.pretrained).lower() == 'true'
+    cuda = device.type != 'cpu'
+
+    # Directories
+    wdir = save_dir / 'weights'
+    wdir.mkdir(parents=True, exist_ok=True)  # make dir
+    last, best = wdir / 'last.pt', wdir / 'best.pt'
+
+    # Save run settings
+    yaml_save(save_dir / 'opt.yaml', vars(opt))
+
+    # Logger
+    logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
+
+    # Download Dataset
+    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
+        data_dir = data if data.is_dir() else (DATASETS_DIR / data)
+        if not data_dir.is_dir():
+            LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
+            t = time.time()
+            if str(data) == 'imagenet':
+                subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
+            else:
+                url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip'
+                download(url, dir=data_dir.parent)
+            s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
+            LOGGER.info(s)
+
+    # Dataloaders
+    nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()])  # number of classes
+    trainloader = create_classification_dataloader(path=data_dir / 'train',
+                                                   imgsz=imgsz,
+                                                   batch_size=bs // WORLD_SIZE,
+                                                   augment=True,
+                                                   cache=opt.cache,
+                                                   rank=LOCAL_RANK,
+                                                   workers=nw)
+
+    test_dir = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val'  # data/test or data/val
+    if RANK in {-1, 0}:
+        testloader = create_classification_dataloader(path=test_dir,
+                                                      imgsz=imgsz,
+                                                      batch_size=bs // WORLD_SIZE * 2,
+                                                      augment=False,
+                                                      cache=opt.cache,
+                                                      rank=-1,
+                                                      workers=nw)
+
+    # Model
+    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
+        if Path(opt.model).is_file() or opt.model.endswith('.pt'):
+            model = attempt_load(opt.model, device='cpu', fuse=False)
+        elif opt.model in torchvision.models.__dict__:  # TorchVision models i.e. resnet50, efficientnet_b0
+            model = torchvision.models.__dict__[opt.model](weights='IMAGENET1K_V1' if pretrained else None)
+        else:
+            m = hub.list('ultralytics/yolov5')  # + hub.list('pytorch/vision')  # models
+            raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m))
+        if isinstance(model, DetectionModel):
+            LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
+            model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10)  # convert to classification model
+        reshape_classifier_output(model, nc)  # update class count
+    for m in model.modules():
+        if not pretrained and hasattr(m, 'reset_parameters'):
+            m.reset_parameters()
+        if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
+            m.p = opt.dropout  # set dropout
+    for p in model.parameters():
+        p.requires_grad = True  # for training
+    model = model.to(device)
+
+    # Info
+    if RANK in {-1, 0}:
+        model.names = trainloader.dataset.classes  # attach class names
+        model.transforms = testloader.dataset.torch_transforms  # attach inference transforms
+        model_info(model)
+        if opt.verbose:
+            LOGGER.info(model)
+        images, labels = next(iter(trainloader))
+        file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / 'train_images.jpg')
+        logger.log_images(file, name='Train Examples')
+        logger.log_graph(model, imgsz)  # log model
+
+    # Optimizer
+    optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay)
+
+    # Scheduler
+    lrf = 0.01  # final lr (fraction of lr0)
+    # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf  # cosine
+    lf = lambda x: (1 - x / epochs) * (1 - lrf) + lrf  # linear
+    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
+    # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
+    #                                    final_div_factor=1 / 25 / lrf)
+
+    # EMA
+    ema = ModelEMA(model) if RANK in {-1, 0} else None
+
+    # DDP mode
+    if cuda and RANK != -1:
+        model = smart_DDP(model)
+
+    # Train
+    t0 = time.time()
+    criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing)  # loss function
+    best_fitness = 0.0
+    scaler = amp.GradScaler(enabled=cuda)
+    val = test_dir.stem  # 'val' or 'test'
+    LOGGER.info(f'Image sizes {imgsz} train, {imgsz} test\n'
+                f'Using {nw * WORLD_SIZE} dataloader workers\n'
+                f"Logging results to {colorstr('bold', save_dir)}\n"
+                f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
+                f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}")
+    for epoch in range(epochs):  # loop over the dataset multiple times
+        tloss, vloss, fitness = 0.0, 0.0, 0.0  # train loss, val loss, fitness
+        model.train()
+        if RANK != -1:
+            trainloader.sampler.set_epoch(epoch)
+        pbar = enumerate(trainloader)
+        if RANK in {-1, 0}:
+            pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT)
+        for i, (images, labels) in pbar:  # progress bar
+            images, labels = images.to(device, non_blocking=True), labels.to(device)
+
+            # Forward
+            with amp.autocast(enabled=cuda):  # stability issues when enabled
+                loss = criterion(model(images), labels)
+
+            # Backward
+            scaler.scale(loss).backward()
+
+            # Optimize
+            scaler.unscale_(optimizer)  # unscale gradients
+            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0)  # clip gradients
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+            if ema:
+                ema.update(model)
+
+            if RANK in {-1, 0}:
+                # Print
+                tloss = (tloss * i + loss.item()) / (i + 1)  # update mean losses
+                mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
+                pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + ' ' * 36
+
+                # Test
+                if i == len(pbar) - 1:  # last batch
+                    top1, top5, vloss = validate.run(model=ema.ema,
+                                                     dataloader=testloader,
+                                                     criterion=criterion,
+                                                     pbar=pbar)  # test accuracy, loss
+                    fitness = top1  # define fitness as top1 accuracy
+
+        # Scheduler
+        scheduler.step()
+
+        # Log metrics
+        if RANK in {-1, 0}:
+            # Best fitness
+            if fitness > best_fitness:
+                best_fitness = fitness
+
+            # Log
+            metrics = {
+                "train/loss": tloss,
+                f"{val}/loss": vloss,
+                "metrics/accuracy_top1": top1,
+                "metrics/accuracy_top5": top5,
+                "lr/0": optimizer.param_groups[0]['lr']}  # learning rate
+            logger.log_metrics(metrics, epoch)
+
+            # Save model
+            final_epoch = epoch + 1 == epochs
+            if (not opt.nosave) or final_epoch:
+                ckpt = {
+                    'epoch': epoch,
+                    'best_fitness': best_fitness,
+                    'model': deepcopy(ema.ema).half(),  # deepcopy(de_parallel(model)).half(),
+                    'ema': None,  # deepcopy(ema.ema).half(),
+                    'updates': ema.updates,
+                    'optimizer': None,  # optimizer.state_dict(),
+                    'opt': vars(opt),
+                    'git': GIT_INFO,  # {remote, branch, commit} if a git repo
+                    'date': datetime.now().isoformat()}
+
+                # Save last, best and delete
+                torch.save(ckpt, last)
+                if best_fitness == fitness:
+                    torch.save(ckpt, best)
+                del ckpt
+
+    # Train complete
+    if RANK in {-1, 0} and final_epoch:
+        LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
+                    f"\nResults saved to {colorstr('bold', save_dir)}"
+                    f"\nPredict:         python classify/predict.py --weights {best} --source im.jpg"
+                    f"\nValidate:        python classify/val.py --weights {best} --data {data_dir}"
+                    f"\nExport:          python export.py --weights {best} --include onnx"
+                    f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
+                    f"\nVisualize:       https://netron.app\n")
+
+        # Plot examples
+        images, labels = (x[:25] for x in next(iter(testloader)))  # first 25 images and labels
+        pred = torch.max(ema.ema(images.to(device)), 1)[1]
+        file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg')
+
+        # Log results
+        meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
+        logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch)
+        logger.log_model(best, epochs, metadata=meta)
+
+
+def parse_opt(known=False):
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path')
+    parser.add_argument('--data', type=str, default='imagenette160', help='cifar10, cifar100, mnist, imagenet, ...')
+    parser.add_argument('--epochs', type=int, default=10, help='total training epochs')
+    parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs')
+    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)')
+    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
+    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
+    parser.add_argument('--project', default=ROOT / 'runs/train-cls', help='save to project/name')
+    parser.add_argument('--name', default='exp', help='save to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--pretrained', nargs='?', const=True, default=True, help='start from i.e. --pretrained False')
+    parser.add_argument('--optimizer', choices=['SGD', 'Adam', 'AdamW', 'RMSProp'], default='Adam', help='optimizer')
+    parser.add_argument('--lr0', type=float, default=0.001, help='initial learning rate')
+    parser.add_argument('--decay', type=float, default=5e-5, help='weight decay')
+    parser.add_argument('--label-smoothing', type=float, default=0.1, help='Label smoothing epsilon')
+    parser.add_argument('--cutoff', type=int, default=None, help='Model layer cutoff index for Classify() head')
+    parser.add_argument('--dropout', type=float, default=None, help='Dropout (fraction)')
+    parser.add_argument('--verbose', action='store_true', help='Verbose mode')
+    parser.add_argument('--seed', type=int, default=0, help='Global training seed')
+    parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
+    return parser.parse_known_args()[0] if known else parser.parse_args()
+
+
+def main(opt):
+    # Checks
+    if RANK in {-1, 0}:
+        print_args(vars(opt))
+        check_git_status()
+        check_requirements()
+
+    # DDP mode
+    device = select_device(opt.device, batch_size=opt.batch_size)
+    if LOCAL_RANK != -1:
+        assert opt.batch_size != -1, 'AutoBatch is coming soon for classification, please pass a valid --batch-size'
+        assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
+        assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
+        torch.cuda.set_device(LOCAL_RANK)
+        device = torch.device('cuda', LOCAL_RANK)
+        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
+
+    # Parameters
+    opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)  # increment run
+
+    # Train
+    train(opt, device)
+
+
+def run(**kwargs):
+    # Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
+    opt = parse_opt(True)
+    for k, v in kwargs.items():
+        setattr(opt, k, v)
+    main(opt)
+    return opt
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

classify/val.py

@@ -0,0 +1,170 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Validate a trained YOLOv5 classification model on a classification dataset
+
+Usage:
+    $ bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate ImageNet
+
+Usage - formats:
+    $ python classify/val.py --weights yolov5s-cls.pt                 # PyTorch
+                                       yolov5s-cls.torchscript        # TorchScript
+                                       yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                       yolov5s-cls_openvino_model     # OpenVINO
+                                       yolov5s-cls.engine             # TensorRT
+                                       yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                       yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                       yolov5s-cls.pb                 # TensorFlow GraphDef
+                                       yolov5s-cls.tflite             # TensorFlow Lite
+                                       yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
+                                       yolov5s-cls_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import create_classification_dataloader
+from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr,
+                           increment_path, print_args)
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    data=ROOT / '../datasets/mnist',  # dataset dir
+    weights=ROOT / 'yolov5s-cls.pt',  # model.pt path(s)
+    batch_size=128,  # batch size
+    imgsz=224,  # inference size (pixels)
+    device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    workers=8,  # max dataloader workers (per RANK in DDP mode)
+    verbose=False,  # verbose output
+    project=ROOT / 'runs/val-cls',  # save to project/name
+    name='exp',  # save to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    model=None,
+    dataloader=None,
+    criterion=None,
+    pbar=None,
+):
+    # Initialize/load model and set device
+    training = model is not None
+    if training:  # called by train.py
+        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model
+        half &= device.type != 'cpu'  # half precision only supported on CUDA
+        model.half() if half else model.float()
+    else:  # called directly
+        device = select_device(device, batch_size=batch_size)
+
+        # Directories
+        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+        save_dir.mkdir(parents=True, exist_ok=True)  # make dir
+
+        # Load model
+        model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
+        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
+        imgsz = check_img_size(imgsz, s=stride)  # check image size
+        half = model.fp16  # FP16 supported on limited backends with CUDA
+        if engine:
+            batch_size = model.batch_size
+        else:
+            device = model.device
+            if not (pt or jit):
+                batch_size = 1  # export.py models default to batch-size 1
+                LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
+
+        # Dataloader
+        data = Path(data)
+        test_dir = data / 'test' if (data / 'test').exists() else data / 'val'  # data/test or data/val
+        dataloader = create_classification_dataloader(path=test_dir,
+                                                      imgsz=imgsz,
+                                                      batch_size=batch_size,
+                                                      augment=False,
+                                                      rank=-1,
+                                                      workers=workers)
+
+    model.eval()
+    pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
+    n = len(dataloader)  # number of batches
+    action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
+    desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
+    bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
+    with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
+        for images, labels in bar:
+            with dt[0]:
+                images, labels = images.to(device, non_blocking=True), labels.to(device)
+
+            with dt[1]:
+                y = model(images)
+
+            with dt[2]:
+                pred.append(y.argsort(1, descending=True)[:, :5])
+                targets.append(labels)
+                if criterion:
+                    loss += criterion(y, labels)
+
+    loss /= n
+    pred, targets = torch.cat(pred), torch.cat(targets)
+    correct = (targets[:, None] == pred).float()
+    acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1)  # (top1, top5) accuracy
+    top1, top5 = acc.mean(0).tolist()
+
+    if pbar:
+        pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
+    if verbose:  # all classes
+        LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
+        LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
+        for i, c in model.names.items():
+            aci = acc[targets == i]
+            top1i, top5i = aci.mean(0).tolist()
+            LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
+
+        # Print results
+        t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt)  # speeds per image
+        shape = (1, 3, imgsz, imgsz)
+        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
+
+    return top1, top5, loss
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path')
+    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)')
+    parser.add_argument('--batch-size', type=int, default=128, help='batch size')
+    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
+    parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output')
+    parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name')
+    parser.add_argument('--name', default='exp', help='save to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+    opt = parser.parse_args()
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(exclude=('tensorboard', 'thop'))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

data/coco.yaml

@@ -0,0 +1,125 @@
+path: ../datasets/coco  # dataset root dir
+train: train2017.txt  # train images (relative to 'path') 118287 images
+val: val2017.txt  # val images (relative to 'path') 5000 images
+test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+
+# stuff names
+stuff_names: [
+  'banner', 'blanket', 'branch', 'bridge', 'building-other', 'bush', 'cabinet', 'cage',
+  'cardboard', 'carpet', 'ceiling-other', 'ceiling-tile', 'cloth', 'clothes', 'clouds', 'counter', 'cupboard',
+  'curtain', 'desk-stuff', 'dirt', 'door-stuff', 'fence', 'floor-marble', 'floor-other', 'floor-stone', 'floor-tile',
+  'floor-wood', 'flower', 'fog', 'food-other', 'fruit', 'furniture-other', 'grass', 'gravel', 'ground-other', 'hill',
+  'house', 'leaves', 'light', 'mat', 'metal', 'mirror-stuff', 'moss', 'mountain', 'mud', 'napkin', 'net', 'paper',
+  'pavement', 'pillow', 'plant-other', 'plastic', 'platform', 'playingfield', 'railing', 'railroad', 'river', 'road',
+  'rock', 'roof', 'rug', 'salad', 'sand', 'sea', 'shelf', 'sky-other', 'skyscraper', 'snow', 'solid-other', 'stairs',
+  'stone', 'straw', 'structural-other', 'table', 'tent', 'textile-other', 'towel', 'tree', 'vegetable', 'wall-brick',
+  'wall-concrete', 'wall-other', 'wall-panel', 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'waterdrops',
+  'window-blind', 'window-other', 'wood',
+  # other
+  'other',
+  # unlabeled
+  'unlabeled'
+]
+
+
+# Download script/URL (optional)
+download: |
+  from utils.general import download, Path
+
+
+  # Download labels
+  #segments = True  # segment or box labels
+  #dir = Path(yaml['path'])  # dataset root dir
+  #url = 'https://github.com/WongKinYiu/yolov7/releases/download/v0.1/'
+  #urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
+  #download(urls, dir=dir.parent)
+
+  # Download data
+  #urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
+  #        'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
+  #        'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
+  #download(urls, dir=dir / 'images', threads=3)

data/hyps/hyp.scratch-high.yaml

@@ -0,0 +1,30 @@
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 7.5  # box loss gain
+cls: 0.5  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 0.7  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+dfl: 1.5  # dfl loss gain
+iou_t: 0.20  # IoU training threshold
+anchor_t: 5.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.9  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.15  # image mixup (probability)
+copy_paste: 0.3  # segment copy-paste (probability)

detect.py

@@ -0,0 +1,231 @@
+import argparse
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLO root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
+from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
+from utils.plots import Annotator, colors, save_one_box
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+        weights=ROOT / 'yolo.pt',  # model path or triton URL
+        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
+        data=ROOT / 'data/coco.yaml',  # dataset.yaml path
+        imgsz=(640, 640),  # inference size (height, width)
+        conf_thres=0.25,  # confidence threshold
+        iou_thres=0.45,  # NMS IOU threshold
+        max_det=1000,  # maximum detections per image
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        view_img=False,  # show results
+        save_txt=False,  # save results to *.txt
+        save_conf=False,  # save confidences in --save-txt labels
+        save_crop=False,  # save cropped prediction boxes
+        nosave=False,  # do not save images/videos
+        classes=None,  # filter by class: --class 0, or --class 0 2 3
+        agnostic_nms=False,  # class-agnostic NMS
+        augment=False,  # augmented inference
+        visualize=False,  # visualize features
+        update=False,  # update all models
+        project=ROOT / 'runs/detect',  # save results to project/name
+        name='exp',  # save results to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        line_thickness=3,  # bounding box thickness (pixels)
+        hide_labels=False,  # hide labels
+        hide_conf=False,  # hide confidences
+        half=False,  # use FP16 half-precision inference
+        dnn=False,  # use OpenCV DNN for ONNX inference
+        vid_stride=1,  # video frame-rate stride
+):
+    source = str(source)
+    save_img = not nosave and not source.endswith('.txt')  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
+    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
+    screenshot = source.lower().startswith('screen')
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
+    else:
+        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.from_numpy(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
+            pred = model(im, augment=augment, visualize=visualize)
+
+        # NMS
+        with dt[2]:
+            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+
+        # Second-stage classifier (optional)
+        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
+
+        # Process predictions
+        for i, det in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f'{i}: '
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
+            s += '%gx%g ' % im.shape[2:]  # print string
+            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+            imc = im0.copy() if save_crop else im0  # for save_crop
+            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
+            if len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
+
+                # Print results
+                for c in det[:, 5].unique():
+                    n = (det[:, 5] == c).sum()  # detections per class
+                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
+
+                # Write results
+                for *xyxy, conf, cls in reversed(det):
+                    if save_txt:  # Write to file
+                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
+                        with open(f'{txt_path}.txt', 'a') as f:
+                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
+                    if save_img or save_crop or view_img:  # Add bbox to image
+                        c = int(cls)  # integer class
+                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
+                        annotator.box_label(xyxy, label, color=colors(c, True))
+                    if save_crop:
+                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == 'Linux' and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == 'image':
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[i].release()  # release previous video writer
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+
+    # Print results
+    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
+    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolo.pt', help='model path or triton URL')
+    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
+    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
+    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
+    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--view-img', action='store_true', help='show results')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
+    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
+    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
+    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+    parser.add_argument('--augment', action='store_true', help='augmented inference')
+    parser.add_argument('--visualize', action='store_true', help='visualize features')
+    parser.add_argument('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
+    parser.add_argument('--name', default='exp', help='save results to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
+    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
+    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    # check_requirements(exclude=('tensorboard', 'thop'))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

detect_dual.py

@@ -0,0 +1,233 @@
+import argparse
+import os
+import platform
+import sys
+from pathlib import Path
+
+
+import torch
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLO root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
+from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
+from utils.plots import Annotator, colors, save_one_box
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+        weights=ROOT / 'yolo.pt',  # model path or triton URL
+        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
+        data=ROOT / 'data/coco.yaml',  # dataset.yaml path
+        imgsz=(640, 640),  # inference size (height, width)
+        conf_thres=0.25,  # confidence threshold
+        iou_thres=0.45,  # NMS IOU threshold
+        max_det=1000,  # maximum detections per image
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        view_img=False,  # show results
+        save_txt=False,  # save results to *.txt
+        save_conf=False,  # save confidences in --save-txt labels
+        save_crop=False,  # save cropped prediction boxes
+        nosave=False,  # do not save images/videos
+        classes=None,  # filter by class: --class 0, or --class 0 2 3
+        agnostic_nms=False,  # class-agnostic NMS
+        augment=False,  # augmented inference
+        visualize=False,  # visualize features
+        update=False,  # update all models
+        project=ROOT / 'runs/detect',  # save results to project/name
+        name='exp',  # save results to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        line_thickness=3,  # bounding box thickness (pixels)
+        hide_labels=False,  # hide labels
+        hide_conf=False,  # hide confidences
+        half=False,  # use FP16 half-precision inference
+        dnn=False,  # use OpenCV DNN for ONNX inference
+        vid_stride=1,  # video frame-rate stride
+):
+    source = str(source)
+    save_img = not nosave and not source.endswith('.txt')  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
+    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
+    screenshot = source.lower().startswith('screen')
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
+    else:
+        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.from_numpy(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
+            pred = model(im, augment=augment, visualize=visualize)
+            pred = pred[0][1]
+
+        # NMS
+        with dt[2]:
+            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+
+        # Second-stage classifier (optional)
+        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
+
+        # Process predictions
+        for i, det in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f'{i}: '
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
+            s += '%gx%g ' % im.shape[2:]  # print string
+            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+            imc = im0.copy() if save_crop else im0  # for save_crop
+            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
+            if len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
+
+                # Print results
+                for c in det[:, 5].unique():
+                    n = (det[:, 5] == c).sum()  # detections per class
+                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
+
+                # Write results
+                for *xyxy, conf, cls in reversed(det):
+                    if save_txt:  # Write to file
+                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
+                        with open(f'{txt_path}.txt', 'a') as f:
+                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
+                    if save_img or save_crop or view_img:  # Add bbox to image
+                        c = int(cls)  # integer class
+                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
+                        annotator.box_label(xyxy, label, color=colors(c, True))
+                    if save_crop:
+                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == 'Linux' and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == 'image':
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[i].release()  # release previous video writer
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+
+    # Print results
+    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
+    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolo.pt', help='model path or triton URL')
+    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
+    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
+    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
+    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--view-img', action='store_true', help='show results')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
+    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
+    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
+    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+    parser.add_argument('--augment', action='store_true', help='augmented inference')
+    parser.add_argument('--visualize', action='store_true', help='visualize features')
+    parser.add_argument('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
+    parser.add_argument('--name', default='exp', help='save results to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
+    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
+    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    # check_requirements(exclude=('tensorboard', 'thop'))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

export.py

@@ -0,0 +1,686 @@
+import argparse
+import contextlib
+import json
+import os
+import platform
+import re
+import subprocess
+import sys
+import time
+import warnings
+from pathlib import Path
+
+import pandas as pd
+import torch
+from torch.utils.mobile_optimizer import optimize_for_mobile
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLO root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+if platform.system() != 'Windows':
+    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.experimental import attempt_load, End2End
+from models.yolo import ClassificationModel, Detect, DDetect, DualDetect, DualDDetect, DetectionModel, SegmentationModel
+from utils.dataloaders import LoadImages
+from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
+                           check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
+from utils.torch_utils import select_device, smart_inference_mode
+
+MACOS = platform.system() == 'Darwin'  # macOS environment
+
+
+def export_formats():
+    # YOLO export formats
+    x = [
+        ['PyTorch', '-', '.pt', True, True],
+        ['TorchScript', 'torchscript', '.torchscript', True, True],
+        ['ONNX', 'onnx', '.onnx', True, True],
+        ['ONNX END2END', 'onnx_end2end', '_end2end.onnx', True, True],
+        ['OpenVINO', 'openvino', '_openvino_model', True, False],
+        ['TensorRT', 'engine', '.engine', False, True],
+        ['CoreML', 'coreml', '.mlmodel', True, False],
+        ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
+        ['TensorFlow GraphDef', 'pb', '.pb', True, True],
+        ['TensorFlow Lite', 'tflite', '.tflite', True, False],
+        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
+        ['TensorFlow.js', 'tfjs', '_web_model', False, False],
+        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
+    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
+
+
+def try_export(inner_func):
+    # YOLO export decorator, i..e @try_export
+    inner_args = get_default_args(inner_func)
+
+    def outer_func(*args, **kwargs):
+        prefix = inner_args['prefix']
+        try:
+            with Profile() as dt:
+                f, model = inner_func(*args, **kwargs)
+            LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)')
+            return f, model
+        except Exception as e:
+            LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
+            return None, None
+
+    return outer_func
+
+
+@try_export
+def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
+    # YOLO TorchScript model export
+    LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
+    f = file.with_suffix('.torchscript')
+
+    ts = torch.jit.trace(model, im, strict=False)
+    d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
+    extra_files = {'config.txt': json.dumps(d)}  # torch._C.ExtraFilesMap()
+    if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
+        optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
+    else:
+        ts.save(str(f), _extra_files=extra_files)
+    return f, None
+
+
+@try_export
+def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
+    # YOLO ONNX export
+    check_requirements('onnx')
+    import onnx
+
+    LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
+    f = file.with_suffix('.onnx')
+
+    output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
+    if dynamic:
+        dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}}  # shape(1,3,640,640)
+        if isinstance(model, SegmentationModel):
+            dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
+            dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'}  # shape(1,32,160,160)
+        elif isinstance(model, DetectionModel):
+            dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
+
+    torch.onnx.export(
+        model.cpu() if dynamic else model,  # --dynamic only compatible with cpu
+        im.cpu() if dynamic else im,
+        f,
+        verbose=False,
+        opset_version=opset,
+        do_constant_folding=True,
+        input_names=['images'],
+        output_names=output_names,
+        dynamic_axes=dynamic or None)
+
+    # Checks
+    model_onnx = onnx.load(f)  # load onnx model
+    onnx.checker.check_model(model_onnx)  # check onnx model
+
+    # Metadata
+    d = {'stride': int(max(model.stride)), 'names': model.names}
+    for k, v in d.items():
+        meta = model_onnx.metadata_props.add()
+        meta.key, meta.value = k, str(v)
+    onnx.save(model_onnx, f)
+
+    # Simplify
+    if simplify:
+        try:
+            cuda = torch.cuda.is_available()
+            check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
+            import onnxsim
+
+            LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
+            model_onnx, check = onnxsim.simplify(model_onnx)
+            assert check, 'assert check failed'
+            onnx.save(model_onnx, f)
+        except Exception as e:
+            LOGGER.info(f'{prefix} simplifier failure: {e}')
+    return f, model_onnx
+    
+
+@try_export
+def export_onnx_end2end(model, im, file, simplify, topk_all, iou_thres, conf_thres, device, labels, prefix=colorstr('ONNX END2END:')):
+    # YOLO ONNX export
+    check_requirements('onnx')
+    import onnx
+    LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
+    f = os.path.splitext(file)[0] + "-end2end.onnx"
+    batch_size = 'batch'
+
+    dynamic_axes = {'images': {0 : 'batch', 2: 'height', 3:'width'}, } # variable length axes
+
+    output_axes = {
+                    'num_dets': {0: 'batch'},
+                    'det_boxes': {0: 'batch'},
+                    'det_scores': {0: 'batch'},
+                    'det_classes': {0: 'batch'},
+                }
+    dynamic_axes.update(output_axes)
+    model = End2End(model, topk_all, iou_thres, conf_thres, None ,device, labels)
+
+    output_names = ['num_dets', 'det_boxes', 'det_scores', 'det_classes']
+    shapes = [ batch_size, 1,  batch_size,  topk_all, 4,
+               batch_size,  topk_all,  batch_size,  topk_all]
+
+    torch.onnx.export(model, 
+                          im, 
+                          f, 
+                          verbose=False, 
+                          export_params=True,       # store the trained parameter weights inside the model file
+                          opset_version=12, 
+                          do_constant_folding=True, # whether to execute constant folding for optimization
+                          input_names=['images'],
+                          output_names=output_names,
+                          dynamic_axes=dynamic_axes)
+
+    # Checks
+    model_onnx = onnx.load(f)  # load onnx model
+    onnx.checker.check_model(model_onnx)  # check onnx model
+    for i in model_onnx.graph.output:
+        for j in i.type.tensor_type.shape.dim:
+            j.dim_param = str(shapes.pop(0))
+
+    if simplify:
+        try:
+            import onnxsim
+
+            print('\nStarting to simplify ONNX...')
+            model_onnx, check = onnxsim.simplify(model_onnx)
+            assert check, 'assert check failed'
+        except Exception as e:
+            print(f'Simplifier failure: {e}')
+
+        # print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable model
+        onnx.save(model_onnx,f)
+        print('ONNX export success, saved as %s' % f)
+    return f, model_onnx
+
+
+@try_export
+def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
+    # YOLO OpenVINO export
+    check_requirements('openvino-dev')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+    import openvino.inference_engine as ie
+
+    LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
+    f = str(file).replace('.pt', f'_openvino_model{os.sep}')
+
+    #cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
+    #cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} {"--compress_to_fp16" if half else ""}"
+    half_arg = "--compress_to_fp16" if half else ""
+    cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} {half_arg}"
+    subprocess.run(cmd.split(), check=True, env=os.environ)  # export
+    yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
+    return f, None
+
+
+@try_export
+def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
+    # YOLO Paddle export
+    check_requirements(('paddlepaddle', 'x2paddle'))
+    import x2paddle
+    from x2paddle.convert import pytorch2paddle
+
+    LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
+    f = str(file).replace('.pt', f'_paddle_model{os.sep}')
+
+    pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im])  # export
+    yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
+    return f, None
+
+
+@try_export
+def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
+    # YOLO CoreML export
+    check_requirements('coremltools')
+    import coremltools as ct
+
+    LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
+    f = file.with_suffix('.mlmodel')
+
+    ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
+    ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
+    bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
+    if bits < 32:
+        if MACOS:  # quantization only supported on macOS
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=DeprecationWarning)  # suppress numpy==1.20 float warning
+                ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
+        else:
+            print(f'{prefix} quantization only supported on macOS, skipping...')
+    ct_model.save(f)
+    return f, ct_model
+
+
+@try_export
+def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
+    # YOLO TensorRT export https://developer.nvidia.com/tensorrt
+    assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
+    try:
+        import tensorrt as trt
+    except Exception:
+        if platform.system() == 'Linux':
+            check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
+        import tensorrt as trt
+
+    if trt.__version__[0] == '7':  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
+        grid = model.model[-1].anchor_grid
+        model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
+        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
+        model.model[-1].anchor_grid = grid
+    else:  # TensorRT >= 8
+        check_version(trt.__version__, '8.0.0', hard=True)  # require tensorrt>=8.0.0
+        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
+    onnx = file.with_suffix('.onnx')
+
+    LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
+    assert onnx.exists(), f'failed to export ONNX file: {onnx}'
+    f = file.with_suffix('.engine')  # TensorRT engine file
+    logger = trt.Logger(trt.Logger.INFO)
+    if verbose:
+        logger.min_severity = trt.Logger.Severity.VERBOSE
+
+    builder = trt.Builder(logger)
+    config = builder.create_builder_config()
+    config.max_workspace_size = workspace * 1 << 30
+    # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice
+
+    flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+    network = builder.create_network(flag)
+    parser = trt.OnnxParser(network, logger)
+    if not parser.parse_from_file(str(onnx)):
+        raise RuntimeError(f'failed to load ONNX file: {onnx}')
+
+    inputs = [network.get_input(i) for i in range(network.num_inputs)]
+    outputs = [network.get_output(i) for i in range(network.num_outputs)]
+    for inp in inputs:
+        LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
+    for out in outputs:
+        LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')
+
+    if dynamic:
+        if im.shape[0] <= 1:
+            LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
+        profile = builder.create_optimization_profile()
+        for inp in inputs:
+            profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
+        config.add_optimization_profile(profile)
+
+    LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}')
+    if builder.platform_has_fast_fp16 and half:
+        config.set_flag(trt.BuilderFlag.FP16)
+    with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
+        t.write(engine.serialize())
+    return f, None
+
+
+@try_export
+def export_saved_model(model,
+                       im,
+                       file,
+                       dynamic,
+                       tf_nms=False,
+                       agnostic_nms=False,
+                       topk_per_class=100,
+                       topk_all=100,
+                       iou_thres=0.45,
+                       conf_thres=0.25,
+                       keras=False,
+                       prefix=colorstr('TensorFlow SavedModel:')):
+    # YOLO TensorFlow SavedModel export
+    try:
+        import tensorflow as tf
+    except Exception:
+        check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
+        import tensorflow as tf
+    from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+
+    from models.tf import TFModel
+
+    LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
+    f = str(file).replace('.pt', '_saved_model')
+    batch_size, ch, *imgsz = list(im.shape)  # BCHW
+
+    tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
+    im = tf.zeros((batch_size, *imgsz, ch))  # BHWC order for TensorFlow
+    _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
+    inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
+    outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
+    keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
+    keras_model.trainable = False
+    keras_model.summary()
+    if keras:
+        keras_model.save(f, save_format='tf')
+    else:
+        spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
+        m = tf.function(lambda x: keras_model(x))  # full model
+        m = m.get_concrete_function(spec)
+        frozen_func = convert_variables_to_constants_v2(m)
+        tfm = tf.Module()
+        tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec])
+        tfm.__call__(im)
+        tf.saved_model.save(tfm,
+                            f,
+                            options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version(
+                                tf.__version__, '2.6') else tf.saved_model.SaveOptions())
+    return f, keras_model
+
+
+@try_export
+def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
+    # YOLO TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
+    import tensorflow as tf
+    from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+
+    LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
+    f = file.with_suffix('.pb')
+
+    m = tf.function(lambda x: keras_model(x))  # full model
+    m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
+    frozen_func = convert_variables_to_constants_v2(m)
+    frozen_func.graph.as_graph_def()
+    tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
+    return f, None
+
+
+@try_export
+def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
+    # YOLOv5 TensorFlow Lite export
+    import tensorflow as tf
+
+    LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
+    batch_size, ch, *imgsz = list(im.shape)  # BCHW
+    f = str(file).replace('.pt', '-fp16.tflite')
+
+    converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
+    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
+    converter.target_spec.supported_types = [tf.float16]
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    if int8:
+        from models.tf import representative_dataset_gen
+        dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
+        converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
+        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+        converter.target_spec.supported_types = []
+        converter.inference_input_type = tf.uint8  # or tf.int8
+        converter.inference_output_type = tf.uint8  # or tf.int8
+        converter.experimental_new_quantizer = True
+        f = str(file).replace('.pt', '-int8.tflite')
+    if nms or agnostic_nms:
+        converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
+
+    tflite_model = converter.convert()
+    open(f, "wb").write(tflite_model)
+    return f, None
+
+
+@try_export
+def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
+    # YOLO Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
+    cmd = 'edgetpu_compiler --version'
+    help_url = 'https://coral.ai/docs/edgetpu/compiler/'
+    assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
+    if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
+        LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
+        sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0  # sudo installed on system
+        for c in (
+                'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
+                'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
+                'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
+            subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
+    ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
+
+    LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
+    f = str(file).replace('.pt', '-int8_edgetpu.tflite')  # Edge TPU model
+    f_tfl = str(file).replace('.pt', '-int8.tflite')  # TFLite model
+
+    cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
+    subprocess.run(cmd.split(), check=True)
+    return f, None
+
+
+@try_export
+def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
+    # YOLO TensorFlow.js export
+    check_requirements('tensorflowjs')
+    import tensorflowjs as tfjs
+
+    LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
+    f = str(file).replace('.pt', '_web_model')  # js dir
+    f_pb = file.with_suffix('.pb')  # *.pb path
+    f_json = f'{f}/model.json'  # *.json path
+
+    cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
+          f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
+    subprocess.run(cmd.split())
+
+    json = Path(f_json).read_text()
+    with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
+        subst = re.sub(
+            r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
+            r'"Identity.?.?": {"name": "Identity.?.?"}, '
+            r'"Identity.?.?": {"name": "Identity.?.?"}, '
+            r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
+            r'"Identity_1": {"name": "Identity_1"}, '
+            r'"Identity_2": {"name": "Identity_2"}, '
+            r'"Identity_3": {"name": "Identity_3"}}}', json)
+        j.write(subst)
+    return f, None
+
+
+def add_tflite_metadata(file, metadata, num_outputs):
+    # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
+    with contextlib.suppress(ImportError):
+        # check_requirements('tflite_support')
+        from tflite_support import flatbuffers
+        from tflite_support import metadata as _metadata
+        from tflite_support import metadata_schema_py_generated as _metadata_fb
+
+        tmp_file = Path('/tmp/meta.txt')
+        with open(tmp_file, 'w') as meta_f:
+            meta_f.write(str(metadata))
+
+        model_meta = _metadata_fb.ModelMetadataT()
+        label_file = _metadata_fb.AssociatedFileT()
+        label_file.name = tmp_file.name
+        model_meta.associatedFiles = [label_file]
+
+        subgraph = _metadata_fb.SubGraphMetadataT()
+        subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
+        subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs
+        model_meta.subgraphMetadata = [subgraph]
+
+        b = flatbuffers.Builder(0)
+        b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
+        metadata_buf = b.Output()
+
+        populator = _metadata.MetadataPopulator.with_model_file(file)
+        populator.load_metadata_buffer(metadata_buf)
+        populator.load_associated_files([str(tmp_file)])
+        populator.populate()
+        tmp_file.unlink()
+
+
+@smart_inference_mode()
+def run(
+        data=ROOT / 'data/coco.yaml',  # 'dataset.yaml path'
+        weights=ROOT / 'yolo.pt',  # weights path
+        imgsz=(640, 640),  # image (height, width)
+        batch_size=1,  # batch size
+        device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        include=('torchscript', 'onnx'),  # include formats
+        half=False,  # FP16 half-precision export
+        inplace=False,  # set YOLO Detect() inplace=True
+        keras=False,  # use Keras
+        optimize=False,  # TorchScript: optimize for mobile
+        int8=False,  # CoreML/TF INT8 quantization
+        dynamic=False,  # ONNX/TF/TensorRT: dynamic axes
+        simplify=False,  # ONNX: simplify model
+        opset=12,  # ONNX: opset version
+        verbose=False,  # TensorRT: verbose log
+        workspace=4,  # TensorRT: workspace size (GB)
+        nms=False,  # TF: add NMS to model
+        agnostic_nms=False,  # TF: add agnostic NMS to model
+        topk_per_class=100,  # TF.js NMS: topk per class to keep
+        topk_all=100,  # TF.js NMS: topk for all classes to keep
+        iou_thres=0.45,  # TF.js NMS: IoU threshold
+        conf_thres=0.25,  # TF.js NMS: confidence threshold
+):
+    t = time.time()
+    include = [x.lower() for x in include]  # to lowercase
+    fmts = tuple(export_formats()['Argument'][1:])  # --include arguments
+    flags = [x in include for x in fmts]
+    assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
+    jit, onnx, onnx_end2end, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags  # export booleans
+    file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)  # PyTorch weights
+
+    # Load PyTorch model
+    device = select_device(device)
+    if half:
+        assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
+        assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
+    model = attempt_load(weights, device=device, inplace=True, fuse=True)  # load FP32 model
+
+    # Checks
+    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
+    if optimize:
+        assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
+
+    # Input
+    gs = int(max(model.stride))  # grid size (max stride)
+    imgsz = [check_img_size(x, gs) for x in imgsz]  # verify img_size are gs-multiples
+    im = torch.zeros(batch_size, 3, *imgsz).to(device)  # image size(1,3,320,192) BCHW iDetection
+
+    # Update model
+    model.eval()
+    for k, m in model.named_modules():
+        if isinstance(m, (Detect, DDetect, DualDetect, DualDDetect)):
+            m.inplace = inplace
+            m.dynamic = dynamic
+            m.export = True
+
+    for _ in range(2):
+        y = model(im)  # dry runs
+    if half and not coreml:
+        im, model = im.half(), model.half()  # to FP16
+    shape = tuple((y[0] if isinstance(y, (tuple, list)) else y).shape)  # model output shape
+    metadata = {'stride': int(max(model.stride)), 'names': model.names}  # model metadata
+    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
+
+    # Exports
+    f = [''] * len(fmts)  # exported filenames
+    warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
+    if jit:  # TorchScript
+        f[0], _ = export_torchscript(model, im, file, optimize)
+    if engine:  # TensorRT required before ONNX
+        f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
+    if onnx or xml:  # OpenVINO requires ONNX
+        f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
+    if onnx_end2end:
+        if isinstance(model, DetectionModel):
+            labels = model.names
+            f[2], _ = export_onnx_end2end(model, im, file, simplify, topk_all, iou_thres, conf_thres, device, len(labels))
+        else:
+            raise RuntimeError("The model is not a DetectionModel.")
+    if xml:  # OpenVINO
+        f[3], _ = export_openvino(file, metadata, half)
+    if coreml:  # CoreML
+        f[4], _ = export_coreml(model, im, file, int8, half)
+    if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
+        assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
+        assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
+        f[5], s_model = export_saved_model(model.cpu(),
+                                           im,
+                                           file,
+                                           dynamic,
+                                           tf_nms=nms or agnostic_nms or tfjs,
+                                           agnostic_nms=agnostic_nms or tfjs,
+                                           topk_per_class=topk_per_class,
+                                           topk_all=topk_all,
+                                           iou_thres=iou_thres,
+                                           conf_thres=conf_thres,
+                                           keras=keras)
+        if pb or tfjs:  # pb prerequisite to tfjs
+            f[6], _ = export_pb(s_model, file)
+        if tflite or edgetpu:
+            f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
+            if edgetpu:
+                f[8], _ = export_edgetpu(file)
+            add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
+        if tfjs:
+            f[9], _ = export_tfjs(file)
+    if paddle:  # PaddlePaddle
+        f[10], _ = export_paddle(model, im, file, metadata)
+
+    # Finish
+    f = [str(x) for x in f if x]  # filter out '' and None
+    if any(f):
+        cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel))  # type
+        dir = Path('segment' if seg else 'classify' if cls else '')
+        h = '--half' if half else ''  # --half FP16 inference arg
+        s = "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" if cls else \
+            "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" if seg else ''
+        if onnx_end2end:
+            LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
+                        f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
+                        f"\nVisualize:       https://netron.app")
+        else:
+            LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
+                        f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
+                        f"\nDetect:          python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
+                        f"\nValidate:        python {dir / 'val.py'} --weights {f[-1]} {h}"
+                        f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')  {s}"
+                        f"\nVisualize:       https://netron.app")
+    return f  # return list of exported files/dirs
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', type=str, default=ROOT / 'data/coco.yaml', help='dataset.yaml path')
+    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolo.pt', help='model.pt path(s)')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
+    parser.add_argument('--inplace', action='store_true', help='set YOLO Detect() inplace=True')
+    parser.add_argument('--keras', action='store_true', help='TF: use Keras')
+    parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
+    parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
+    parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
+    parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
+    parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
+    parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
+    parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
+    parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
+    parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model')
+    parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
+    parser.add_argument('--topk-all', type=int, default=100, help='ONNX END2END/TF.js NMS: topk for all classes to keep')
+    parser.add_argument('--iou-thres', type=float, default=0.45, help='ONNX END2END/TF.js NMS: IoU threshold')
+    parser.add_argument('--conf-thres', type=float, default=0.25, help='ONNX END2END/TF.js NMS: confidence threshold')
+    parser.add_argument(
+        '--include',
+        nargs='+',
+        default=['torchscript'],
+        help='torchscript, onnx, onnx_end2end, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle')
+    opt = parser.parse_args()
+
+    if 'onnx_end2end' in opt.include:  
+        opt.simplify = True
+        opt.dynamic = True
+        opt.inplace = True
+        opt.half = False
+
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]):
+        run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

hubconf.py

@@ -0,0 +1,107 @@
+import torch
+
+
+def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+    """Creates or loads a YOLO model
+
+    Arguments:
+        name (str): model name 'yolov3' or path 'path/to/best.pt'
+        pretrained (bool): load pretrained weights into the model
+        channels (int): number of input channels
+        classes (int): number of model classes
+        autoshape (bool): apply YOLO .autoshape() wrapper to model
+        verbose (bool): print all information to screen
+        device (str, torch.device, None): device to use for model parameters
+
+    Returns:
+        YOLO model
+    """
+    from pathlib import Path
+
+    from models.common import AutoShape, DetectMultiBackend
+    from models.experimental import attempt_load
+    from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
+    from utils.downloads import attempt_download
+    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
+    from utils.torch_utils import select_device
+
+    if not verbose:
+        LOGGER.setLevel(logging.WARNING)
+    check_requirements(exclude=('opencv-python', 'tensorboard', 'thop'))
+    name = Path(name)
+    path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name  # checkpoint path
+    try:
+        device = select_device(device)
+        if pretrained and channels == 3 and classes == 80:
+            try:
+                model = DetectMultiBackend(path, device=device, fuse=autoshape)  # detection model
+                if autoshape:
+                    if model.pt and isinstance(model.model, ClassificationModel):
+                        LOGGER.warning('WARNING ⚠️ YOLO ClassificationModel is not yet AutoShape compatible. '
+                                       'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
+                    elif model.pt and isinstance(model.model, SegmentationModel):
+                        LOGGER.warning('WARNING ⚠️ YOLO SegmentationModel is not yet AutoShape compatible. '
+                                       'You will not be able to run inference with this model.')
+                    else:
+                        model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
+            except Exception:
+                model = attempt_load(path, device=device, fuse=False)  # arbitrary model
+        else:
+            cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0]  # model.yaml path
+            model = DetectionModel(cfg, channels, classes)  # create model
+            if pretrained:
+                ckpt = torch.load(attempt_download(path), map_location=device)  # load
+                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
+                csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors'])  # intersect
+                model.load_state_dict(csd, strict=False)  # load
+                if len(ckpt['model'].names) == classes:
+                    model.names = ckpt['model'].names  # set class names attribute
+        if not verbose:
+            LOGGER.setLevel(logging.INFO)  # reset to default
+        return model.to(device)
+
+    except Exception as e:
+        help_url = 'https://github.com/ultralytics/yolov5/issues/36'
+        s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
+        raise Exception(s) from e
+
+
+def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None):
+    # YOLO custom or local model
+    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
+
+
+if __name__ == '__main__':
+    import argparse
+    from pathlib import Path
+
+    import numpy as np
+    from PIL import Image
+
+    from utils.general import cv2, print_args
+
+    # Argparser
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str, default='yolo', help='model name')
+    opt = parser.parse_args()
+    print_args(vars(opt))
+
+    # Model
+    model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
+    # model = custom(path='path/to/model.pt')  # custom
+
+    # Images
+    imgs = [
+        'data/images/zidane.jpg',  # filename
+        Path('data/images/zidane.jpg'),  # Path
+        'https://ultralytics.com/images/zidane.jpg',  # URI
+        cv2.imread('data/images/bus.jpg')[:, :, ::-1],  # OpenCV
+        Image.open('data/images/bus.jpg'),  # PIL
+        np.zeros((320, 640, 3))]  # numpy
+
+    # Inference
+    results = model(imgs, size=320)  # batched inference
+
+    # Results
+    results.print()
+    results.save()

models/__init__.py

@@ -0,0 +1 @@
+# init

models/common.py

@@ -0,0 +1,1280 @@
+import ast
+import contextlib
+import json
+import math
+import platform
+import warnings
+import zipfile
+from collections import OrderedDict, namedtuple
+from copy import copy
+from pathlib import Path
+from urllib.parse import urlparse
+
+from typing import Optional
+
+import cv2
+import numpy as np
+import pandas as pd
+import requests
+import torch
+import torch.nn as nn
+from IPython.display import display
+from PIL import Image
+from torch.cuda import amp
+
+from utils import TryExcept
+from utils.dataloaders import exif_transpose, letterbox
+from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
+                           increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes,
+                           xywh2xyxy, xyxy2xywh, yaml_load)
+from utils.plots import Annotator, colors, save_one_box
+from utils.torch_utils import copy_attr, smart_inference_mode
+
+
+def autopad(k, p=None, d=1):  # kernel, padding, dilation
+    # Pad to 'same' shape outputs
+    if d > 1:
+        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
+    if p is None:
+        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
+    return p
+
+
+class Conv(nn.Module):
+    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
+        super().__init__()
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        return self.act(self.conv(x))
+
+
+class AConv(nn.Module):
+    def __init__(self, c1, c2):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        self.cv1 = Conv(c1, c2, 3, 2, 1)
+
+    def forward(self, x):
+        x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)
+        return self.cv1(x)
+
+
+class ADown(nn.Module):
+    def __init__(self, c1, c2):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        self.c = c2 // 2
+        self.cv1 = Conv(c1 // 2, self.c, 3, 2, 1)
+        self.cv2 = Conv(c1 // 2, self.c, 1, 1, 0)
+
+    def forward(self, x):
+        x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)
+        x1,x2 = x.chunk(2, 1)
+        x1 = self.cv1(x1)
+        x2 = torch.nn.functional.max_pool2d(x2, 3, 2, 1)
+        x2 = self.cv2(x2)
+        return torch.cat((x1, x2), 1)
+
+
+class RepConvN(nn.Module):
+    """RepConv is a basic rep-style block, including training and deploy status
+    This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
+    """
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False):
+        super().__init__()
+        assert k == 3 and p == 1
+        self.g = g
+        self.c1 = c1
+        self.c2 = c2
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+        self.bn = None
+        self.conv1 = Conv(c1, c2, k, s, p=p, g=g, act=False)
+        self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False)
+
+    def forward_fuse(self, x):
+        """Forward process"""
+        return self.act(self.conv(x))
+
+    def forward(self, x):
+        """Forward process"""
+        id_out = 0 if self.bn is None else self.bn(x)
+        return self.act(self.conv1(x) + self.conv2(x) + id_out)
+
+    def get_equivalent_kernel_bias(self):
+        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
+        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2)
+        kernelid, biasid = self._fuse_bn_tensor(self.bn)
+        return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
+
+    def _avg_to_3x3_tensor(self, avgp):
+        channels = self.c1
+        groups = self.g
+        kernel_size = avgp.kernel_size
+        input_dim = channels // groups
+        k = torch.zeros((channels, input_dim, kernel_size, kernel_size))
+        k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2
+        return k
+
+    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
+        if kernel1x1 is None:
+            return 0
+        else:
+            return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
+
+    def _fuse_bn_tensor(self, branch):
+        if branch is None:
+            return 0, 0
+        if isinstance(branch, Conv):
+            kernel = branch.conv.weight
+            running_mean = branch.bn.running_mean
+            running_var = branch.bn.running_var
+            gamma = branch.bn.weight
+            beta = branch.bn.bias
+            eps = branch.bn.eps
+        elif isinstance(branch, nn.BatchNorm2d):
+            if not hasattr(self, 'id_tensor'):
+                input_dim = self.c1 // self.g
+                kernel_value = np.zeros((self.c1, input_dim, 3, 3), dtype=np.float32)
+                for i in range(self.c1):
+                    kernel_value[i, i % input_dim, 1, 1] = 1
+                self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
+            kernel = self.id_tensor
+            running_mean = branch.running_mean
+            running_var = branch.running_var
+            gamma = branch.weight
+            beta = branch.bias
+            eps = branch.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
+
+    def fuse_convs(self):
+        if hasattr(self, 'conv'):
+            return
+        kernel, bias = self.get_equivalent_kernel_bias()
+        self.conv = nn.Conv2d(in_channels=self.conv1.conv.in_channels,
+                              out_channels=self.conv1.conv.out_channels,
+                              kernel_size=self.conv1.conv.kernel_size,
+                              stride=self.conv1.conv.stride,
+                              padding=self.conv1.conv.padding,
+                              dilation=self.conv1.conv.dilation,
+                              groups=self.conv1.conv.groups,
+                              bias=True).requires_grad_(False)
+        self.conv.weight.data = kernel
+        self.conv.bias.data = bias
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__('conv1')
+        self.__delattr__('conv2')
+        if hasattr(self, 'nm'):
+            self.__delattr__('nm')
+        if hasattr(self, 'bn'):
+            self.__delattr__('bn')
+        if hasattr(self, 'id_tensor'):
+            self.__delattr__('id_tensor')
+
+
+class SP(nn.Module):
+    def __init__(self, k=3, s=1):
+        super(SP, self).__init__()
+        self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)
+
+    def forward(self, x):
+        return self.m(x)
+
+
+class MP(nn.Module):
+    # Max pooling
+    def __init__(self, k=2):
+        super(MP, self).__init__()
+        self.m = nn.MaxPool2d(kernel_size=k, stride=k)
+
+    def forward(self, x):
+        return self.m(x)
+
+
+class ConvTranspose(nn.Module):
+    # Convolution transpose 2d layer
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True):
+        super().__init__()
+        self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn)
+        self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity()
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv_transpose(x)))
+
+
+class DWConv(Conv):
+    # Depth-wise convolution
+    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
+        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
+
+
+class DWConvTranspose2d(nn.ConvTranspose2d):
+    # Depth-wise transpose convolution
+    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
+        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
+
+
+class DFL(nn.Module):
+    # DFL module
+    def __init__(self, c1=17):
+        super().__init__()
+        self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
+        self.conv.weight.data[:] = nn.Parameter(torch.arange(c1, dtype=torch.float).view(1, c1, 1, 1)) # / 120.0
+        self.c1 = c1
+        # self.bn = nn.BatchNorm2d(4)
+
+    def forward(self, x):
+        b, c, a = x.shape  # batch, channels, anchors
+        return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a)
+        # return self.conv(x.view(b, self.c1, 4, a).softmax(1)).view(b, 4, a)
+
+
+class BottleneckBase(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(1, 3), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class RBottleneckBase(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 1), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class RepNRBottleneckBase(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 1), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = RepConvN(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class Bottleneck(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class RepNBottleneck(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = RepConvN(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class Res(nn.Module):
+    # ResNet bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
+        super(Res, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_, c_, 3, 1, g=g)
+        self.cv3 = Conv(c_, c2, 1, 1)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x)))
+
+
+class RepNRes(nn.Module):
+    # ResNet bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
+        super(RepNRes, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = RepConvN(c_, c_, 3, 1, g=g)
+        self.cv3 = Conv(c_, c2, 1, 1)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x)))
+
+
+class BottleneckCSP(nn.Module):
+    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+        self.cv4 = Conv(2 * c_, c2, 1, 1)
+        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
+        self.act = nn.SiLU()
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+    def forward(self, x):
+        y1 = self.cv3(self.m(self.cv1(x)))
+        y2 = self.cv2(x)
+        return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
+
+
+class CSP(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+    def forward(self, x):
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
+
+
+class RepNCSP(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
+        self.m = nn.Sequential(*(RepNBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+    def forward(self, x):
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
+
+
+class CSPBase(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
+        self.m = nn.Sequential(*(BottleneckBase(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+    def forward(self, x):
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
+
+
+class SPP(nn.Module):
+    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
+    def __init__(self, c1, c2, k=(5, 9, 13)):
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
+        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
+            return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
+
+        
+class ASPP(torch.nn.Module):
+
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        kernel_sizes = [1, 3, 3, 1]
+        dilations = [1, 3, 6, 1]
+        paddings = [0, 3, 6, 0]
+        self.aspp = torch.nn.ModuleList()
+        for aspp_idx in range(len(kernel_sizes)):
+            conv = torch.nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=kernel_sizes[aspp_idx],
+                stride=1,
+                dilation=dilations[aspp_idx],
+                padding=paddings[aspp_idx],
+                bias=True)
+            self.aspp.append(conv)
+        self.gap = torch.nn.AdaptiveAvgPool2d(1)
+        self.aspp_num = len(kernel_sizes)
+        for m in self.modules():
+            if isinstance(m, torch.nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+                m.bias.data.fill_(0)
+
+    def forward(self, x):
+        avg_x = self.gap(x)
+        out = []
+        for aspp_idx in range(self.aspp_num):
+            inp = avg_x if (aspp_idx == self.aspp_num - 1) else x
+            out.append(F.relu_(self.aspp[aspp_idx](inp)))
+        out[-1] = out[-1].expand_as(out[-2])
+        out = torch.cat(out, dim=1)
+        return out
+
+
+class SPPCSPC(nn.Module):
+    # CSP SPP https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
+        super(SPPCSPC, self).__init__()
+        c_ = int(2 * c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(c_, c_, 3, 1)
+        self.cv4 = Conv(c_, c_, 1, 1)
+        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+        self.cv5 = Conv(4 * c_, c_, 1, 1)
+        self.cv6 = Conv(c_, c_, 3, 1)
+        self.cv7 = Conv(2 * c_, c2, 1, 1)
+
+    def forward(self, x):
+        x1 = self.cv4(self.cv3(self.cv1(x)))
+        y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
+        y2 = self.cv2(x)
+        return self.cv7(torch.cat((y1, y2), dim=1))
+
+
+class SPPF(nn.Module):
+    # Spatial Pyramid Pooling - Fast (SPPF) layer by Glenn Jocher
+    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * 4, c2, 1, 1)
+        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
+        # self.m = SoftPool2d(kernel_size=k, stride=1, padding=k // 2)
+
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
+            y1 = self.m(x)
+            y2 = self.m(y1)
+            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
+
+
+import torch.nn.functional as F
+from torch.nn.modules.utils import _pair
+    
+    
+class ReOrg(nn.Module):
+    # yolo
+    def __init__(self):
+        super(ReOrg, self).__init__()
+
+    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+        return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
+
+
+class Contract(nn.Module):
+    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
+        s = self.gain
+        x = x.view(b, c, h // s, s, w // s, s)  # x(1,64,40,2,40,2)
+        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
+        return x.view(b, c * s * s, h // s, w // s)  # x(1,256,40,40)
+
+
+class Expand(nn.Module):
+    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
+        s = self.gain
+        x = x.view(b, s, s, c // s ** 2, h, w)  # x(1,2,2,16,80,80)
+        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
+        return x.view(b, c // s ** 2, h * s, w * s)  # x(1,16,160,160)
+
+
+class Concat(nn.Module):
+    # Concatenate a list of tensors along dimension
+    def __init__(self, dimension=1):
+        super().__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return torch.cat(x, self.d)
+
+
+class Shortcut(nn.Module):
+    def __init__(self, dimension=0):
+        super(Shortcut, self).__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return x[0]+x[1]
+    
+    
+class Silence(nn.Module):
+    def __init__(self):
+        super(Silence, self).__init__()
+    def forward(self, x):    
+        return x
+
+
+##### GELAN #####        
+        
+class SPPELAN(nn.Module):
+    # spp-elan
+    def __init__(self, c1, c2, c3):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        self.c = c3
+        self.cv1 = Conv(c1, c3, 1, 1)
+        self.cv2 = SP(5)
+        self.cv3 = SP(5)
+        self.cv4 = SP(5)
+        self.cv5 = Conv(4*c3, c2, 1, 1)
+
+    def forward(self, x):
+        y = [self.cv1(x)]
+        y.extend(m(y[-1]) for m in [self.cv2, self.cv3, self.cv4])
+        return self.cv5(torch.cat(y, 1))
+        
+        
+class ELAN1(nn.Module):
+
+    def __init__(self, c1, c2, c3, c4):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        self.c = c3//2
+        self.cv1 = Conv(c1, c3, 1, 1)
+        self.cv2 = Conv(c3//2, c4, 3, 1)
+        self.cv3 = Conv(c4, c4, 3, 1)
+        self.cv4 = Conv(c3+(2*c4), c2, 1, 1)
+
+    def forward(self, x):
+        y = list(self.cv1(x).chunk(2, 1))
+        y.extend(m(y[-1]) for m in [self.cv2, self.cv3])
+        return self.cv4(torch.cat(y, 1))
+
+    def forward_split(self, x):
+        y = list(self.cv1(x).split((self.c, self.c), 1))
+        y.extend(m(y[-1]) for m in [self.cv2, self.cv3])
+        return self.cv4(torch.cat(y, 1))
+        
+        
+class RepNCSPELAN4(nn.Module):
+    # csp-elan
+    def __init__(self, c1, c2, c3, c4, c5=1):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        self.c = c3//2
+        self.cv1 = Conv(c1, c3, 1, 1)
+        self.cv2 = nn.Sequential(RepNCSP(c3//2, c4, c5), Conv(c4, c4, 3, 1))
+        self.cv3 = nn.Sequential(RepNCSP(c4, c4, c5), Conv(c4, c4, 3, 1))
+        self.cv4 = Conv(c3+(2*c4), c2, 1, 1)
+
+    def forward(self, x):
+        y = list(self.cv1(x).chunk(2, 1))
+        y.extend((m(y[-1])) for m in [self.cv2, self.cv3])
+        return self.cv4(torch.cat(y, 1))
+
+    def forward_split(self, x):
+        y = list(self.cv1(x).split((self.c, self.c), 1))
+        y.extend(m(y[-1]) for m in [self.cv2, self.cv3])
+        return self.cv4(torch.cat(y, 1))
+
+#################
+#####add new model####
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8,
+                 attn_ratio=0.5):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.key_dim = int(self.head_dim * attn_ratio)
+        self.scale = self.key_dim ** -0.5
+        nh_kd = nh_kd = self.key_dim * num_heads
+        h = dim + nh_kd * 2
+        self.qkv = Conv(dim, h, 1, act=False)
+        self.proj = Conv(dim, dim, 1, act=False)
+        self.pe = Conv(dim, dim, 3, 1, g=dim, act=False)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        N = H * W
+        qkv = self.qkv(x)
+        q, k, v = qkv.view(B, self.num_heads, self.key_dim*2 + self.head_dim, N).split([self.key_dim, self.key_dim, self.head_dim], dim=2)
+
+        attn = (
+            (q.transpose(-2, -1) @ k) * self.scale
+        )
+        attn = attn.softmax(dim=-1)
+        x = (v @ attn.transpose(-2, -1)).view(B, C, H, W) + self.pe(v.reshape(B, C, H, W))
+        x = self.proj(x)
+        return x
+
+class PSA(nn.Module):
+
+    def __init__(self, c1, c2, e=0.5):
+        super().__init__()
+        assert(c1 == c2)
+        self.c = int(c1 * e)
+        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
+        self.cv2 = Conv(2 * self.c, c1, 1)
+
+        self.attn = Attention(self.c, attn_ratio=0.5, num_heads=self.c // 64)
+        self.ffn = nn.Sequential(
+            Conv(self.c, self.c*2, 1),
+            Conv(self.c*2, self.c, 1, act=False)
+        )
+    def forward(self, x):
+            a, b = self.cv1(x).split((self.c, self.c), dim=1)
+            b = b + self.attn(b)
+            b = b + self.ffn(b)
+            return self.cv2(torch.cat((a, b), 1))
+
+##### YOLOR #####
+
+class ImplicitA(nn.Module):
+    def __init__(self, channel):
+        super(ImplicitA, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
+        nn.init.normal_(self.implicit, std=.02)        
+
+    def forward(self, x):
+        return self.implicit + x
+
+
+class ImplicitM(nn.Module):
+    def __init__(self, channel):
+        super(ImplicitM, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1))
+        nn.init.normal_(self.implicit, mean=1., std=.02)        
+
+    def forward(self, x):
+        return self.implicit * x
+
+#################
+
+
+##### CBNet #####
+
+class CBLinear(nn.Module):
+    def __init__(self, c1, c2s, k=1, s=1, p=None, g=1):  # ch_in, ch_outs, kernel, stride, padding, groups
+        super(CBLinear, self).__init__()
+        self.c2s = c2s
+        self.conv = nn.Conv2d(c1, sum(c2s), k, s, autopad(k, p), groups=g, bias=True)
+
+    def forward(self, x):
+        outs = self.conv(x).split(self.c2s, dim=1)
+        return outs
+
+class CBFuse(nn.Module):
+    def __init__(self, idx):
+        super(CBFuse, self).__init__()
+        self.idx = idx
+
+    def forward(self, xs):
+        target_size = xs[-1].shape[2:]
+        res = [F.interpolate(x[self.idx[i]], size=target_size, mode='nearest') for i, x in enumerate(xs[:-1])]
+        out = torch.sum(torch.stack(res + xs[-1:]), dim=0)
+        return out
+
+#################
+
+
+class DetectMultiBackend(nn.Module):
+    # YOLO MultiBackend class for python inference on various backends
+    def __init__(self, weights='yolo.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
+        # Usage:
+        #   PyTorch:              weights = *.pt
+        #   TorchScript:                    *.torchscript
+        #   ONNX Runtime:                   *.onnx
+        #   ONNX OpenCV DNN:                *.onnx --dnn
+        #   OpenVINO:                       *_openvino_model
+        #   CoreML:                         *.mlmodel
+        #   TensorRT:                       *.engine
+        #   TensorFlow SavedModel:          *_saved_model
+        #   TensorFlow GraphDef:            *.pb
+        #   TensorFlow Lite:                *.tflite
+        #   TensorFlow Edge TPU:            *_edgetpu.tflite
+        #   PaddlePaddle:                   *_paddle_model
+        from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
+
+        super().__init__()
+        w = str(weights[0] if isinstance(weights, list) else weights)
+        pt, jit, onnx, onnx_end2end, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
+        fp16 &= pt or jit or onnx or engine  # FP16
+        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
+        stride = 32  # default stride
+        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
+        if not (pt or triton):
+            w = attempt_download(w)  # download if not local
+
+        if pt:  # PyTorch
+            model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
+            stride = max(int(model.stride.max()), 32)  # model stride
+            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
+            model.half() if fp16 else model.float()
+            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
+        elif jit:  # TorchScript
+            LOGGER.info(f'Loading {w} for TorchScript inference...')
+            extra_files = {'config.txt': ''}  # model metadata
+            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
+            model.half() if fp16 else model.float()
+            if extra_files['config.txt']:  # load metadata dict
+                d = json.loads(extra_files['config.txt'],
+                               object_hook=lambda d: {int(k) if k.isdigit() else k: v
+                                                      for k, v in d.items()})
+                stride, names = int(d['stride']), d['names']
+        elif dnn:  # ONNX OpenCV DNN
+            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
+            check_requirements('opencv-python>=4.5.4')
+            net = cv2.dnn.readNetFromONNX(w)
+        elif onnx:  # ONNX Runtime
+            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
+            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
+            import onnxruntime
+            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
+            session = onnxruntime.InferenceSession(w, providers=providers)
+            output_names = [x.name for x in session.get_outputs()]
+            meta = session.get_modelmeta().custom_metadata_map  # metadata
+            if 'stride' in meta:
+                stride, names = int(meta['stride']), eval(meta['names'])
+        elif xml:  # OpenVINO
+            LOGGER.info(f'Loading {w} for OpenVINO inference...')
+            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+            from openvino.runtime import Core, Layout, get_batch
+            ie = Core()
+            if not Path(w).is_file():  # if not *.xml
+                w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
+            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
+            if network.get_parameters()[0].get_layout().empty:
+                network.get_parameters()[0].set_layout(Layout("NCHW"))
+            batch_dim = get_batch(network)
+            if batch_dim.is_static:
+                batch_size = batch_dim.get_length()
+            executable_network = ie.compile_model(network, device_name="CPU")  # device_name="MYRIAD" for Intel NCS2
+            stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
+        elif engine:  # TensorRT
+            LOGGER.info(f'Loading {w} for TensorRT inference...')
+            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
+            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
+            if device.type == 'cpu':
+                device = torch.device('cuda:0')
+            Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
+            logger = trt.Logger(trt.Logger.INFO)
+            with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
+                model = runtime.deserialize_cuda_engine(f.read())
+            context = model.create_execution_context()
+            bindings = OrderedDict()
+            output_names = []
+            fp16 = False  # default updated below
+            dynamic = False
+            for i in range(model.num_bindings):
+                name = model.get_binding_name(i)
+                dtype = trt.nptype(model.get_binding_dtype(i))
+                if model.binding_is_input(i):
+                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
+                        dynamic = True
+                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
+                    if dtype == np.float16:
+                        fp16 = True
+                else:  # output
+                    output_names.append(name)
+                shape = tuple(context.get_binding_shape(i))
+                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
+                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
+            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
+            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
+        elif coreml:  # CoreML
+            LOGGER.info(f'Loading {w} for CoreML inference...')
+            import coremltools as ct
+            model = ct.models.MLModel(w)
+        elif saved_model:  # TF SavedModel
+            LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
+            import tensorflow as tf
+            keras = False  # assume TF1 saved_model
+            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
+        elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
+            LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
+            import tensorflow as tf
+
+            def wrap_frozen_graph(gd, inputs, outputs):
+                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
+                ge = x.graph.as_graph_element
+                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
+
+            def gd_outputs(gd):
+                name_list, input_list = [], []
+                for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
+                    name_list.append(node.name)
+                    input_list.extend(node.input)
+                return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
+
+            gd = tf.Graph().as_graph_def()  # TF GraphDef
+            with open(w, 'rb') as f:
+                gd.ParseFromString(f.read())
+            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
+        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
+            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
+                from tflite_runtime.interpreter import Interpreter, load_delegate
+            except ImportError:
+                import tensorflow as tf
+                Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
+            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
+                LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
+                delegate = {
+                    'Linux': 'libedgetpu.so.1',
+                    'Darwin': 'libedgetpu.1.dylib',
+                    'Windows': 'edgetpu.dll'}[platform.system()]
+                interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
+            else:  # TFLite
+                LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
+                interpreter = Interpreter(model_path=w)  # load TFLite model
+            interpreter.allocate_tensors()  # allocate
+            input_details = interpreter.get_input_details()  # inputs
+            output_details = interpreter.get_output_details()  # outputs
+            # load metadata
+            with contextlib.suppress(zipfile.BadZipFile):
+                with zipfile.ZipFile(w, "r") as model:
+                    meta_file = model.namelist()[0]
+                    meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
+                    stride, names = int(meta['stride']), meta['names']
+        elif tfjs:  # TF.js
+            raise NotImplementedError('ERROR: YOLO TF.js inference is not supported')
+        elif paddle:  # PaddlePaddle
+            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
+            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
+            import paddle.inference as pdi
+            if not Path(w).is_file():  # if not *.pdmodel
+                w = next(Path(w).rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
+            weights = Path(w).with_suffix('.pdiparams')
+            config = pdi.Config(str(w), str(weights))
+            if cuda:
+                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
+            predictor = pdi.create_predictor(config)
+            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
+            output_names = predictor.get_output_names()
+        elif triton:  # NVIDIA Triton Inference Server
+            LOGGER.info(f'Using {w} as Triton Inference Server...')
+            check_requirements('tritonclient[all]')
+            from utils.triton import TritonRemoteModel
+            model = TritonRemoteModel(url=w)
+            nhwc = model.runtime.startswith("tensorflow")
+        else:
+            raise NotImplementedError(f'ERROR: {w} is not a supported format')
+
+        # class names
+        if 'names' not in locals():
+            names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
+        if names[0] == 'n01440764' and len(names) == 1000:  # ImageNet
+            names = yaml_load(ROOT / 'data/ImageNet.yaml')['names']  # human-readable names
+
+        self.__dict__.update(locals())  # assign all variables to self
+
+    def forward(self, im, augment=False, visualize=False):
+        # YOLO MultiBackend inference
+        b, ch, h, w = im.shape  # batch, channel, height, width
+        if self.fp16 and im.dtype != torch.float16:
+            im = im.half()  # to FP16
+        if self.nhwc:
+            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)
+
+        if self.pt:  # PyTorch
+            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
+        elif self.jit:  # TorchScript
+            y = self.model(im)
+        elif self.dnn:  # ONNX OpenCV DNN
+            im = im.cpu().numpy()  # torch to numpy
+            self.net.setInput(im)
+            y = self.net.forward()
+        elif self.onnx:  # ONNX Runtime
+            im = im.cpu().numpy()  # torch to numpy
+            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
+        elif self.xml:  # OpenVINO
+            im = im.cpu().numpy()  # FP32
+            y = list(self.executable_network([im]).values())
+        elif self.engine:  # TensorRT
+            if self.dynamic and im.shape != self.bindings['images'].shape:
+                i = self.model.get_binding_index('images')
+                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
+                self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
+                for name in self.output_names:
+                    i = self.model.get_binding_index(name)
+                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
+            s = self.bindings['images'].shape
+            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
+            self.binding_addrs['images'] = int(im.data_ptr())
+            self.context.execute_v2(list(self.binding_addrs.values()))
+            y = [self.bindings[x].data for x in sorted(self.output_names)]
+        elif self.coreml:  # CoreML
+            im = im.cpu().numpy()
+            im = Image.fromarray((im[0] * 255).astype('uint8'))
+            # im = im.resize((192, 320), Image.ANTIALIAS)
+            y = self.model.predict({'image': im})  # coordinates are xywh normalized
+            if 'confidence' in y:
+                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
+                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
+                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
+            else:
+                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
+        elif self.paddle:  # PaddlePaddle
+            im = im.cpu().numpy().astype(np.float32)
+            self.input_handle.copy_from_cpu(im)
+            self.predictor.run()
+            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
+        elif self.triton:  # NVIDIA Triton Inference Server
+            y = self.model(im)
+        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
+            im = im.cpu().numpy()
+            if self.saved_model:  # SavedModel
+                y = self.model(im, training=False) if self.keras else self.model(im)
+            elif self.pb:  # GraphDef
+                y = self.frozen_func(x=self.tf.constant(im))
+            else:  # Lite or Edge TPU
+                input = self.input_details[0]
+                int8 = input['dtype'] == np.uint8  # is TFLite quantized uint8 model
+                if int8:
+                    scale, zero_point = input['quantization']
+                    im = (im / scale + zero_point).astype(np.uint8)  # de-scale
+                self.interpreter.set_tensor(input['index'], im)
+                self.interpreter.invoke()
+                y = []
+                for output in self.output_details:
+                    x = self.interpreter.get_tensor(output['index'])
+                    if int8:
+                        scale, zero_point = output['quantization']
+                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
+                    y.append(x)
+            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
+            y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels
+
+        if isinstance(y, (list, tuple)):
+            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
+        else:
+            return self.from_numpy(y)
+
+    def from_numpy(self, x):
+        return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
+
+    def warmup(self, imgsz=(1, 3, 640, 640)):
+        # Warmup model by running inference once
+        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
+        if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
+            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
+            for _ in range(2 if self.jit else 1):  #
+                self.forward(im)  # warmup
+
+    @staticmethod
+    def _model_type(p='path/to/model.pt'):
+        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
+        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
+        from export import export_formats
+        from utils.downloads import is_url
+        sf = list(export_formats().Suffix)  # export suffixes
+        if not is_url(p, check=False):
+            check_suffix(p, sf)  # checks
+        url = urlparse(p)  # if url may be Triton inference server
+        types = [s in Path(p).name for s in sf]
+        types[8] &= not types[9]  # tflite &= not edgetpu
+        triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
+        return types + [triton]
+
+    @staticmethod
+    def _load_metadata(f=Path('path/to/meta.yaml')):
+        # Load metadata from meta.yaml if it exists
+        if f.exists():
+            d = yaml_load(f)
+            return d['stride'], d['names']  # assign stride, names
+        return None, None
+
+
+class AutoShape(nn.Module):
+    # YOLO input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    conf = 0.25  # NMS confidence threshold
+    iou = 0.45  # NMS IoU threshold
+    agnostic = False  # NMS class-agnostic
+    multi_label = False  # NMS multiple labels per box
+    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
+    max_det = 1000  # maximum number of detections per image
+    amp = False  # Automatic Mixed Precision (AMP) inference
+
+    def __init__(self, model, verbose=True):
+        super().__init__()
+        if verbose:
+            LOGGER.info('Adding AutoShape... ')
+        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
+        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
+        self.pt = not self.dmb or model.pt  # PyTorch model
+        self.model = model.eval()
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.inplace = False  # Detect.inplace=False for safe multithread inference
+            m.export = True  # do not output loss values
+
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        from models.yolo import Detect, Segment
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            if isinstance(m, (Detect, Segment)):
+                for k in 'stride', 'anchor_grid', 'stride_grid', 'grid':
+                    x = getattr(m, k)
+                    setattr(m, k, list(map(fn, x))) if isinstance(x, (list, tuple)) else setattr(m, k, fn(x))
+        return self
+
+    @smart_inference_mode()
+    def forward(self, ims, size=640, augment=False, profile=False):
+        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
+        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
+        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
+        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
+        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
+        #   numpy:           = np.zeros((640,1280,3))  # HWC
+        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
+        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
+
+        dt = (Profile(), Profile(), Profile())
+        with dt[0]:
+            if isinstance(size, int):  # expand
+                size = (size, size)
+            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
+            autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
+            if isinstance(ims, torch.Tensor):  # torch
+                with amp.autocast(autocast):
+                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
+
+            # Pre-process
+            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
+            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
+            for i, im in enumerate(ims):
+                f = f'image{i}'  # filename
+                if isinstance(im, (str, Path)):  # filename or uri
+                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
+                    im = np.asarray(exif_transpose(im))
+                elif isinstance(im, Image.Image):  # PIL Image
+                    im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
+                files.append(Path(f).with_suffix('.jpg').name)
+                if im.shape[0] < 5:  # image in CHW
+                    im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
+                im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
+                s = im.shape[:2]  # HWC
+                shape0.append(s)  # image shape
+                g = max(size) / max(s)  # gain
+                shape1.append([int(y * g) for y in s])
+                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
+            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)]  # inf shape
+            x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
+            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
+            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
+
+        with amp.autocast(autocast):
+            # Inference
+            with dt[1]:
+                y = self.model(x, augment=augment)  # forward
+
+            # Post-process
+            with dt[2]:
+                y = non_max_suppression(y if self.dmb else y[0],
+                                        self.conf,
+                                        self.iou,
+                                        self.classes,
+                                        self.agnostic,
+                                        self.multi_label,
+                                        max_det=self.max_det)  # NMS
+                for i in range(n):
+                    scale_boxes(shape1, y[i][:, :4], shape0[i])
+
+            return Detections(ims, y, files, dt, self.names, x.shape)
+
+
+class Detections:
+    # YOLO detections class for inference results
+    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
+        super().__init__()
+        d = pred[0].device  # device
+        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizations
+        self.ims = ims  # list of images as numpy arrays
+        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
+        self.names = names  # class names
+        self.files = files  # image filenames
+        self.times = times  # profiling times
+        self.xyxy = pred  # xyxy pixels
+        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
+        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
+        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
+        self.n = len(self.pred)  # number of images (batch size)
+        self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
+        self.s = tuple(shape)  # inference BCHW shape
+
+    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
+        s, crops = '', []
+        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
+            s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
+            if pred.shape[0]:
+                for c in pred[:, -1].unique():
+                    n = (pred[:, -1] == c).sum()  # detections per class
+                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
+                s = s.rstrip(', ')
+                if show or save or render or crop:
+                    annotator = Annotator(im, example=str(self.names))
+                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
+                        label = f'{self.names[int(cls)]} {conf:.2f}'
+                        if crop:
+                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
+                            crops.append({
+                                'box': box,
+                                'conf': conf,
+                                'cls': cls,
+                                'label': label,
+                                'im': save_one_box(box, im, file=file, save=save)})
+                        else:  # all others
+                            annotator.box_label(box, label if labels else '', color=colors(cls))
+                    im = annotator.im
+            else:
+                s += '(no detections)'
+
+            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
+            if show:
+                display(im) if is_notebook() else im.show(self.files[i])
+            if save:
+                f = self.files[i]
+                im.save(save_dir / f)  # save
+                if i == self.n - 1:
+                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
+            if render:
+                self.ims[i] = np.asarray(im)
+        if pprint:
+            s = s.lstrip('\n')
+            return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
+        if crop:
+            if save:
+                LOGGER.info(f'Saved results to {save_dir}\n')
+            return crops
+
+    @TryExcept('Showing images is not supported in this environment')
+    def show(self, labels=True):
+        self._run(show=True, labels=labels)  # show results
+
+    def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
+        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
+        self._run(save=True, labels=labels, save_dir=save_dir)  # save results
+
+    def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
+        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
+        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results
+
+    def render(self, labels=True):
+        self._run(render=True, labels=labels)  # render results
+        return self.ims
+
+    def pandas(self):
+        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
+        new = copy(self)  # return copy
+        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
+        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
+        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
+            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
+            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
+        return new
+
+    def tolist(self):
+        # return a list of Detections objects, i.e. 'for result in results.tolist():'
+        r = range(self.n)  # iterable
+        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
+        # for d in x:
+        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
+        return x
+
+    def print(self):
+        LOGGER.info(self.__str__())
+
+    def __len__(self):  # override len(results)
+        return self.n
+
+    def __str__(self):  # override print(results)
+        return self._run(pprint=True)  # print results
+
+    def __repr__(self):
+        return f'YOLO {self.__class__} instance\n' + self.__str__()
+
+
+class Proto(nn.Module):
+    # YOLO mask Proto module for segmentation models
+    def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
+        super().__init__()
+        self.cv1 = Conv(c1, c_, k=3)
+        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
+        self.cv2 = Conv(c_, c_, k=3)
+        self.cv3 = Conv(c_, c2)
+
+    def forward(self, x):
+        return self.cv3(self.cv2(self.upsample(self.cv1(x))))
+
+
+class UConv(nn.Module):
+    def __init__(self, c1, c_=256, c2=256):  # ch_in, number of protos, number of masks
+        super().__init__()
+        
+        self.cv1 = Conv(c1, c_, k=3)
+        self.cv2 = nn.Conv2d(c_, c2, 1, 1)
+        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
+
+    def forward(self, x):
+        return self.up(self.cv2(self.cv1(x)))
+
+
+class Classify(nn.Module):
+    # YOLO classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
+        super().__init__()
+        c_ = 1280  # efficientnet_b0 size
+        self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
+        self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
+        self.drop = nn.Dropout(p=0.0, inplace=True)
+        self.linear = nn.Linear(c_, c2)  # to x(b,c2)
+
+    def forward(self, x):
+        if isinstance(x, list):
+            x = torch.cat(x, 1)
+        return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))

models/detect/gelan-c.yaml

@@ -0,0 +1,80 @@
+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 2
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 4
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 6
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 8
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 15 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 21 (P5/32-large)
+
+   # detect
+   [[15, 18, 21], 1, DDetect, [nc]],  # DDetect(P3, P4, P5)
+  ]

models/detect/gelan-e.yaml

@@ -0,0 +1,121 @@
+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   [-1, 1, Silence, []],
+  
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 2-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]],  # 3
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 4-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]],  # 5
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 6-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 7
+
+   # avg-conv down
+   [-1, 1, ADown, [1024]],  # 8-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 9
+   
+   # routing
+   [1, 1, CBLinear, [[64]]], # 10
+   [3, 1, CBLinear, [[64, 128]]], # 11
+   [5, 1, CBLinear, [[64, 128, 256]]], # 12
+   [7, 1, CBLinear, [[64, 128, 256, 512]]], # 13
+   [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]], # 14
+  
+   # conv down fuse
+   [0, 1, Conv, [64, 3, 2]],  # 15-P1/2
+   [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]], # 16
+
+   # conv down fuse
+   [-1, 1, Conv, [128, 3, 2]],  # 17-P2/4
+   [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]], # 18  
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]],  # 19
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [256]],  # 20-P3/8
+   [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]], # 21  
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]],  # 22
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [512]],  # 23-P4/16
+   [[13, 14, -1], 1, CBFuse, [[3, 3]]], # 24 
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 25
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [1024]],  # 26-P5/32
+   [[14, -1], 1, CBFuse, [[4]]], # 27
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 28
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [28, 1, SPPELAN, [512, 256]],  # 29
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 25], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]],  # 32
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 22], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]],  # 35 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 32], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]],  # 38 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 29], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]],  # 41 (P5/32-large)
+
+   # detect
+   [[35, 38, 41], 1, DDetect, [nc]],  # Detect(P3, P4, P5)
+  ]

models/detect/gelan-m.yaml

@@ -0,0 +1,80 @@
+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [32, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [128, 128, 64, 1]],  # 2
+
+   # avg-conv down
+   [-1, 1, AConv, [240]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [240, 240, 120, 1]],  # 4
+
+   # avg-conv down
+   [-1, 1, AConv, [360]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]],  # 6
+
+   # avg-conv down
+   [-1, 1, AConv, [480]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [480, 480, 240, 1]],  # 8
+  ]
+
+# elan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [480, 240]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [240, 240, 120, 1]],  # 15
+
+   # avg-conv-down merge
+   [-1, 1, AConv, [180]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, AConv, [240]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [480, 480, 240, 1]],  # 21 (P5/32-large)
+
+   # detect
+   [[15, 18, 21], 1, DDetect, [nc]],  # DDetect(P3, P4, P5)
+  ]

models/detect/gelan-s.yaml

@@ -0,0 +1,80 @@
+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [32, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, ELAN1, [64, 64, 32]],  # 2
+
+   # avg-conv down
+   [-1, 1, AConv, [128]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]],  # 4
+
+   # avg-conv down
+   [-1, 1, AConv, [192]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]],  # 6
+
+   # avg-conv down
+   [-1, 1, AConv, [256]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 3]],  # 8
+  ]
+
+# elan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [256, 128]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]],  # 15
+
+   # avg-conv-down merge
+   [-1, 1, AConv, [96]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, AConv, [128]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 3]],  # 21 (P5/32-large)
+
+   # detect
+   [[15, 18, 21], 1, DDetect, [nc]],  # DDetect(P3, P4, P5)
+  ]

models/detect/gelan.yaml

@@ -0,0 +1,80 @@
+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 2
+
+   # avg-conv down
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 4
+
+   # avg-conv down
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 6
+
+   # avg-conv down
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 8
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 15 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 21 (P5/32-large)
+
+   # detect
+   [[15, 18, 21], 1, DDetect, [nc]],  # Detect(P3, P4, P5)
+  ]

models/detect/yolo9trGPR.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61043d59cab99e8be75be00d4219d0627e63da26be061b3762aa07a0e8e8d9c7
+size 21298882

models/detect/yolov7-af.yaml

@@ -0,0 +1,137 @@
+# YOLOv7
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 1.  # model depth multiple
+width_multiple: 1.  # layer channel multiple
+anchors: 3
+
+# YOLOv7 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+  
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      
+   [-1, 1, Conv, [64, 3, 1]],
+   
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  
+   [-1, 1, Conv, [64, 1, 1]],
+   [-2, 1, Conv, [64, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]],  # 11
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [128, 1, 1]],
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 16-P3/8  
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [512, 1, 1]],  # 24
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 29-P4/16  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [1024, 1, 1]],  # 37
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-3, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 42-P5/32  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [1024, 1, 1]],  # 50
+  ]
+
+# yolov7 head
+head:
+  [[-1, 1, SPPCSPC, [512]], # 51
+  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [37, 1, Conv, [256, 1, 1]], # route backbone P4
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]], # 63
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [24, 1, Conv, [128, 1, 1]], # route backbone P3
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [128, 1, 1]], # 75
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [128, 1, 1]],
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, -3, 63], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]], # 88
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, -3, 51], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [512, 1, 1]],
+   [-2, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [512, 1, 1]], # 101
+   
+   [75, 1, Conv, [256, 3, 1]],
+   [88, 1, Conv, [512, 3, 1]],
+   [101, 1, Conv, [1024, 3, 1]],
+
+   [[102, 103, 104], 1, Detect, [nc]],  # Detect(P3, P4, P5)
+  ]