Upload 10 files

deployment/Detection task/README.md

@@ -0,0 +1,151 @@
+# Exportable code
+
+Exportable code is a .zip archive that contains simple demo to get and visualize result of model inference.
+
+## Structure of generated zip
+
+- `README.md`
+- model
+  - `model.xml`
+  - `model.bin`
+  - `config.json`
+- python
+  - model_wrappers (Optional)
+    - `__init__.py`
+    - model_wrappers required to run demo
+  - `LICENSE`
+  - `demo.py`
+  - `requirements.txt`
+
+> **NOTE**: Zip archive contains model_wrappers when [ModelAPI](https://github.com/openvinotoolkit/model_api) has no appropriate standard model wrapper for the model.
+
+## Prerequisites
+
+- [Python 3.8](https://www.python.org/downloads/)
+- [Git](https://git-scm.com/)
+
+## Install requirements to run demo
+
+1. Install [prerequisites](#prerequisites). You may also need to [install pip](https://pip.pypa.io/en/stable/installation/). For example, on Ubuntu execute the following command to get pip installed:
+
+   ```bash
+   sudo apt install python3-pip
+   ```
+
+1. Create clean virtual environment:
+
+   One of the possible ways for creating a virtual environment is to use `virtualenv`:
+
+   ```bash
+   python -m pip install virtualenv
+   python -m virtualenv <directory_for_environment>
+   ```
+
+   Before starting to work inside virtual environment, it should be activated:
+
+   On Linux and macOS:
+
+   ```bash
+   source <directory_for_environment>/bin/activate
+   ```
+
+   On Windows:
+
+   ```bash
+   .\<directory_for_environment>\Scripts\activate
+   ```
+
+   Please make sure that the environment contains [wheel](https://pypi.org/project/wheel/) by calling the following command:
+
+   ```bash
+   python -m pip install wheel
+   ```
+
+   > **NOTE**: On Linux and macOS, you may need to type `python3` instead of `python`.
+
+1. Install requirements in the environment:
+
+   ```bash
+   python -m pip install -r requirements.txt
+   ```
+
+## Usecase
+
+1. Running the `demo.py` application with the `-h` option yields the following usage message:
+
+   ```bash
+   usage: demo.py [-h] -i INPUT -m MODELS [MODELS ...] [-it {sync,async}] [-l] [--no_show] [-d {CPU,GPU}] [--output OUTPUT]
+
+   Options:
+   -h, --help            Show this help message and exit.
+   -i INPUT, --input INPUT
+                           Required. An input to process. The input must be a single image, a folder of images, video file or camera id.
+   -m MODELS [MODELS ...], --models MODELS [MODELS ...]
+                           Optional. Path to directory with trained model and configuration file. If you provide several models you will start the task chain pipeline with the provided models in the order in which they were specified. Default value points to deployed model folder '../model'.
+   -it {sync,async}, --inference_type {sync,async}
+                           Optional. Type of inference for single model.
+   -l, --loop            Optional. Enable reading the input in a loop.
+   --no_show             Optional. Disables showing inference results on UI.
+   -d {CPU,GPU}, --device {CPU,GPU}
+                           Optional. Device to infer the model.
+   --output OUTPUT       Optional. Output path to save input data with predictions.
+   ```
+
+2. As a `model` parameter the default value `../model` will be used. Or you can specify the other path to the model directory from generated zip. You can pass as `input` a single image, a folder of images, a video file, or a web camera id. So you can use the following command to do inference with a pre-trained model:
+
+   ```bash
+   python3 demo.py -i <path_to_video>/inputVideo.mp4
+   ```
+
+   You can press `Q` to stop inference during demo running.
+
+   > **NOTE**: If you provide a single image as input, the demo processes and renders it quickly, then exits. To continuously
+   > visualize inference results on the screen, apply the `--loop` option, which enforces processing a single image in a loop.
+   > In this case, you can stop the demo by pressing `Q` button or killing the process in the terminal (`Ctrl+C` for Linux).
+   >
+   > **NOTE**: Default configuration contains info about pre- and post processing for inference and is guaranteed to be correct.
+   > Also you can change `config.json` that specifies the confidence threshold and color for each class visualization, but any
+   > changes should be made with caution.
+
+3. To save inferenced results with predictions on it, you can specify the folder path, using `--output`.
+   It works for images, videos, image folders and web cameras. To prevent issues, do not specify it together with a `--loop` parameter.
+
+   ```bash
+   python3 demo.py \
+      --input <path_to_image>/inputImage.jpg \
+      --models ../model \
+      --output resulted_images
+   ```
+
+4. To run a demo on a web camera, you need to know its ID.
+   You can check a list of camera devices by running this command line on Linux system:
+
+   ```bash
+   sudo apt-get install v4l-utils
+   v4l2-ctl --list-devices
+   ```
+
+   The output will look like this:
+
+   ```bash
+   Integrated Camera (usb-0000:00:1a.0-1.6):
+      /dev/video0
+   ```
+
+   After that, you can use this `/dev/video0` as a camera ID for `--input`.
+
+## Troubleshooting
+
+1. If you have access to the Internet through the proxy server only, please use pip with proxy call as demonstrated by command below:
+
+   ```bash
+   python -m pip install --proxy http://<usr_name>:<password>@<proxyserver_name>:<port#> <pkg_name>
+   ```
+
+1. If you use Anaconda environment, you should consider that OpenVINO has limited [Conda support](https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_conda.html) for Python 3.6 and 3.7 versions only. But the demo package requires python 3.8. So please use other tools to create the environment (like `venv` or `virtualenv`) and use `pip` as a package manager.
+
+1. If you have problems when you try to use `pip install` command, please update pip version by following command:
+
+   ```bash
+   python -m pip install --upgrade pip
+   ```

deployment/Detection task/model.json

@@ -0,0 +1,26 @@
+{
+   "id": "671f86a824640b8373989a71",
+   "name": "YOLOX-TINY OpenVINO FP16",
+   "version": 5,
+   "creation_date": "2024-10-28T12:42:16.659000+00:00",
+   "model_format": "OpenVINO",
+   "precision": [
+      "FP16"
+   ],
+   "has_xai_head": false,
+   "target_device": "CPU",
+   "target_device_type": null,
+   "performance": {
+      "score": 0.9401041666666665
+   },
+   "size": 10679671,
+   "latency": 0,
+   "fps_throughput": 0,
+   "optimization_type": "MO",
+   "optimization_objectives": {},
+   "model_status": "SUCCESS",
+   "configurations": [],
+   "previous_revision_id": "671f86a824640b8373989a6e",
+   "previous_trained_revision_id": "671f86a824640b8373989a6e",
+   "optimization_methods": []
+}

deployment/Detection task/model/config.json

@@ -0,0 +1,102 @@
+{
+    "type_of_model": "SSD",
+    "converter_type": "DETECTION",
+    "model_parameters": {
+        "result_based_confidence_threshold": true,
+        "confidence_threshold": 0.699999988079071,
+        "nms_iou_threshold": 0.5,
+        "max_num_detections": 0,
+        "use_ellipse_shapes": false,
+        "resize_type": "fit_to_window_letterbox",
+        "pad_value": 114,
+        "labels": {
+            "label_tree": {
+                "type": "tree",
+                "directed": true,
+                "nodes": [
+                    "6483613d18fb8c1c529cb061",
+                    "6483613d18fb8c1c529cb065"
+                ],
+                "edges": []
+            },
+            "label_groups": [
+                {
+                    "_id": "671f86cb2da8d99626c678a1",
+                    "name": "Default group",
+                    "label_ids": [
+                        "6483613d18fb8c1c529cb061"
+                    ],
+                    "relation_type": "EXCLUSIVE"
+                },
+                {
+                    "_id": "671f86cb2da8d99626c678a2",
+                    "name": "No Object",
+                    "label_ids": [
+                        "6483613d18fb8c1c529cb065"
+                    ],
+                    "relation_type": "EMPTY_LABEL"
+                }
+            ],
+            "all_labels": {
+                "6483613d18fb8c1c529cb061": {
+                    "_id": "6483613d18fb8c1c529cb061",
+                    "name": "bird",
+                    "color": {
+                        "red": 255,
+                        "green": 0,
+                        "blue": 0,
+                        "alpha": 255
+                    },
+                    "hotkey": "",
+                    "domain": "DETECTION",
+                    "creation_date": "2024-10-28T12:42:51.839000",
+                    "is_empty": false,
+                    "is_anomalous": false
+                },
+                "6483613d18fb8c1c529cb065": {
+                    "_id": "6483613d18fb8c1c529cb065",
+                    "name": "No Object",
+                    "color": {
+                        "red": 0,
+                        "green": 0,
+                        "blue": 0,
+                        "alpha": 255
+                    },
+                    "hotkey": "",
+                    "domain": "DETECTION",
+                    "creation_date": "2024-10-28T12:42:51.839000",
+                    "is_empty": true,
+                    "is_anomalous": false
+                }
+            }
+        }
+    },
+    "tiling_parameters": {
+        "visible_in_ui": true,
+        "type": "PARAMETER_GROUP",
+        "enable_tiling": false,
+        "enable_tile_classifier": false,
+        "enable_adaptive_params": true,
+        "tile_size": 400,
+        "tile_overlap": 0.2,
+        "tile_max_number": 1500,
+        "tile_ir_scale_factor": 1.0,
+        "tile_sampling_ratio": 1.0,
+        "object_tile_ratio": 0.03,
+        "header": "Tiling Parameters",
+        "description": "Tiling Parameters",
+        "_ParameterGroup__metadata_overrides": {},
+        "groups": [],
+        "parameters": [
+            "enable_adaptive_params",
+            "enable_tile_classifier",
+            "enable_tiling",
+            "object_tile_ratio",
+            "tile_ir_scale_factor",
+            "tile_max_number",
+            "tile_overlap",
+            "tile_sampling_ratio",
+            "tile_size"
+        ]
+    }
+}

deployment/Detection task/model/model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d1c5c9882eeeb92b1a50bb3f33a2105a2ced1ca3c0fc5ee20b3223b72b0fd5d
+size 10048860

deployment/Detection task/python/LICENSE

@@ -0,0 +1,201 @@
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction,
+and distribution as defined by Sections 1 through 9 of this document.
+
+"Licensor" shall mean the copyright owner or entity authorized by
+the copyright owner that is granting the License.
+
+"Legal Entity" shall mean the union of the acting entity and all
+other entities that control, are controlled by, or are under common
+control with that entity. For the purposes of this definition,
+"control" means (i) the power, direct or indirect, to cause the
+direction or management of such entity, whether by contract or
+otherwise, or (ii) ownership of fifty percent (50%) or more of the
+outstanding shares, or (iii) beneficial ownership of such entity.
+
+"You" (or "Your") shall mean an individual or Legal Entity
+exercising permissions granted by this License.
+
+"Source" form shall mean the preferred form for making modifications,
+including but not limited to software source code, documentation
+source, and configuration files.
+
+"Object" form shall mean any form resulting from mechanical
+transformation or translation of a Source form, including but
+not limited to compiled object code, generated documentation,
+and conversions to other media types.
+
+"Work" shall mean the work of authorship, whether in Source or
+Object form, made available under the License, as indicated by a
+copyright notice that is included in or attached to the work
+(an example is provided in the Appendix below).
+
+"Derivative Works" shall mean any work, whether in Source or Object
+form, that is based on (or derived from) the Work and for which the
+editorial revisions, annotations, elaborations, or other modifications
+represent, as a whole, an original work of authorship. For the purposes
+of this License, Derivative Works shall not include works that remain
+separable from, or merely link (or bind by name) to the interfaces of,
+the Work and Derivative Works thereof.
+
+"Contribution" shall mean any work of authorship, including
+the original version of the Work and any modifications or additions
+to that Work or Derivative Works thereof, that is intentionally
+submitted to Licensor for inclusion in the Work by the copyright owner
+or by an individual or Legal Entity authorized to submit on behalf of
+the copyright owner. For the purposes of this definition, "submitted"
+means any form of electronic, verbal, or written communication sent
+to the Licensor or its representatives, including but not limited to
+communication on electronic mailing lists, source code control systems,
+and issue tracking systems that are managed by, or on behalf of, the
+Licensor for the purpose of discussing and improving the Work, but
+excluding communication that is conspicuously marked or otherwise
+designated in writing by the copyright owner as "Not a Contribution."
+
+"Contributor" shall mean Licensor and any individual or Legal Entity
+on behalf of whom a Contribution has been received by Licensor and
+subsequently incorporated within the Work.
+
+2. Grant of Copyright License. Subject to the terms and conditions of
+this License, each Contributor hereby grants to You a perpetual,
+worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+copyright license to reproduce, prepare Derivative Works of,
+publicly display, publicly perform, sublicense, and distribute the
+Work and such Derivative Works in Source or Object form.
+
+3. Grant of Patent License. Subject to the terms and conditions of
+this License, each Contributor hereby grants to You a perpetual,
+worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+(except as stated in this section) patent license to make, have made,
+use, offer to sell, sell, import, and otherwise transfer the Work,
+where such license applies only to those patent claims licensable
+by such Contributor that are necessarily infringed by their
+Contribution(s) alone or by combination of their Contribution(s)
+with the Work to which such Contribution(s) was submitted. If You
+institute patent litigation against any entity (including a
+cross-claim or counterclaim in a lawsuit) alleging that the Work
+or a Contribution incorporated within the Work constitutes direct
+or contributory patent infringement, then any patent licenses
+granted to You under this License for that Work shall terminate
+as of the date such litigation is filed.
+
+4. Redistribution. You may reproduce and distribute copies of the
+Work or Derivative Works thereof in any medium, with or without
+modifications, and in Source or Object form, provided that You
+meet the following conditions:
+
+(a) You must give any other recipients of the Work or
+Derivative Works a copy of this License; and
+
+(b) You must cause any modified files to carry prominent notices
+stating that You changed the files; and
+
+(c) You must retain, in the Source form of any Derivative Works
+that You distribute, all copyright, patent, trademark, and
+attribution notices from the Source form of the Work,
+excluding those notices that do not pertain to any part of
+the Derivative Works; and
+
+(d) If the Work includes a "NOTICE" text file as part of its
+distribution, then any Derivative Works that You distribute must
+include a readable copy of the attribution notices contained
+within such NOTICE file, excluding those notices that do not
+pertain to any part of the Derivative Works, in at least one
+of the following places: within a NOTICE text file distributed
+as part of the Derivative Works; within the Source form or
+documentation, if provided along with the Derivative Works; or,
+within a display generated by the Derivative Works, if and
+wherever such third-party notices normally appear. The contents
+of the NOTICE file are for informational purposes only and
+do not modify the License. You may add Your own attribution
+notices within Derivative Works that You distribute, alongside
+or as an addendum to the NOTICE text from the Work, provided
+that such additional attribution notices cannot be construed
+as modifying the License.
+
+You may add Your own copyright statement to Your modifications and
+may provide additional or different license terms and conditions
+for use, reproduction, or distribution of Your modifications, or
+for any such Derivative Works as a whole, provided Your use,
+reproduction, and distribution of the Work otherwise complies with
+the conditions stated in this License.
+
+5. Submission of Contributions. Unless You explicitly state otherwise,
+any Contribution intentionally submitted for inclusion in the Work
+by You to the Licensor shall be under the terms and conditions of
+this License, without any additional terms or conditions.
+Notwithstanding the above, nothing herein shall supersede or modify
+the terms of any separate license agreement you may have executed
+with Licensor regarding such Contributions.
+
+6. Trademarks. This License does not grant permission to use the trade
+names, trademarks, service marks, or product names of the Licensor,
+except as required for reasonable and customary use in describing the
+origin of the Work and reproducing the content of the NOTICE file.
+
+7. Disclaimer of Warranty. Unless required by applicable law or
+agreed to in writing, Licensor provides the Work (and each
+Contributor provides its Contributions) on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied, including, without limitation, any warranties or conditions
+of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+PARTICULAR PURPOSE. You are solely responsible for determining the
+appropriateness of using or redistributing the Work and assume any
+risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability. In no event and under no legal theory,
+whether in tort (including negligence), contract, or otherwise,
+unless required by applicable law (such as deliberate and grossly
+negligent acts) or agreed to in writing, shall any Contributor be
+liable to You for damages, including any direct, indirect, special,
+incidental, or consequential damages of any character arising as a
+result of this License or out of the use or inability to use the
+Work (including but not limited to damages for loss of goodwill,
+work stoppage, computer failure or malfunction, or any and all
+other commercial damages or losses), even if such Contributor
+has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability. While redistributing
+the Work or Derivative Works thereof, You may choose to offer,
+and charge a fee for, acceptance of support, warranty, indemnity,
+or other liability obligations and/or rights consistent with this
+License. However, in accepting such obligations, You may act only
+on Your own behalf and on Your sole responsibility, not on behalf
+of any other Contributor, and only if You agree to indemnify,
+defend, and hold each Contributor harmless for any liability
+incurred by, or claims asserted against, such Contributor by reason
+of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
+
+APPENDIX: How to apply the Apache License to your work.
+
+To apply the Apache License to your work, attach the following
+boilerplate notice, with the fields enclosed by brackets "[]"
+replaced with your own identifying information. (Don't include
+the brackets!)  The text should be enclosed in the appropriate
+comment syntax for the file format. We also recommend that a
+file or class name and description of purpose be included on the
+same "printed page" as the copyright notice for easier
+identification within third-party archives.
+
+Copyright (C) 2018-2021 Intel Corporation
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

deployment/Detection task/python/demo.py

@@ -0,0 +1,133 @@
+"""Demo based on ModelAPI."""
+# Copyright (C) 2021-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import os
+import sys
+from argparse import SUPPRESS, ArgumentParser
+from pathlib import Path
+
+os.environ["FEATURE_FLAGS_OTX_ACTION_TASKS"] = "1"
+
+# pylint: disable=no-name-in-module, import-error
+from otx.api.usecases.exportable_code.demo.demo_package import (
+    AsyncExecutor,
+    ChainExecutor,
+    ModelContainer,
+    SyncExecutor,
+    create_visualizer,
+)
+
+
+def build_argparser():
+    """Parses command line arguments."""
+    parser = ArgumentParser(add_help=False)
+    args = parser.add_argument_group("Options")
+    args.add_argument(
+        "-h",
+        "--help",
+        action="help",
+        default=SUPPRESS,
+        help="Show this help message and exit.",
+    )
+    args.add_argument(
+        "-i",
+        "--input",
+        required=True,
+        help="Required. An input to process. The input must be a single image, "
+        "a folder of images, video file or camera id.",
+    )
+    args.add_argument(
+        "-m",
+        "--models",
+        help="Optional. Path to directory with trained model and configuration file. "
+        "If you provide several models you will start the task chain pipeline with "
+        "the provided models in the order in which they were specified. Default value "
+        "points to deployed model folder '../model'.",
+        nargs="+",
+        default=[Path("../model")],
+        type=Path,
+    )
+    args.add_argument(
+        "-it",
+        "--inference_type",
+        help="Optional. Type of inference for single model.",
+        choices=["sync", "async"],
+        default="sync",
+        type=str,
+    )
+    args.add_argument(
+        "-l",
+        "--loop",
+        help="Optional. Enable reading the input in a loop.",
+        default=False,
+        action="store_true",
+    )
+    args.add_argument(
+        "--no_show",
+        help="Optional. Disables showing inference results on UI.",
+        default=False,
+        action="store_true",
+    )
+    args.add_argument(
+        "-d",
+        "--device",
+        help="Optional. Device to infer the model.",
+        choices=["CPU", "GPU"],
+        default="CPU",
+        type=str,
+    )
+    args.add_argument(
+        "--output",
+        default=None,
+        type=str,
+        help="Optional. Output path to save input data with predictions.",
+    )
+
+    return parser
+
+
+EXECUTORS = {
+    "sync": SyncExecutor,
+    "async": AsyncExecutor,
+    "chain": ChainExecutor,
+}
+
+
+def get_inferencer_class(type_inference, models):
+    """Return class for inference of models."""
+    if len(models) > 1:
+        type_inference = "chain"
+        print("You started the task chain pipeline with the provided models in the order in which they were specified")
+    return EXECUTORS[type_inference]
+
+
+def main():
+    """Main function that is used to run demo."""
+    args = build_argparser().parse_args()
+
+    if args.loop and args.output:
+        raise ValueError("--loop and --output cannot be both specified")
+
+    # create models
+    models = []
+    for model_dir in args.models:
+        model = ModelContainer(model_dir, device=args.device)
+        models.append(model)
+
+    inferencer = get_inferencer_class(args.inference_type, models)
+
+    # create visualizer
+    visualizer = create_visualizer(models[-1].task_type, no_show=args.no_show, output=args.output)
+
+    if len(models) == 1:
+        models = models[0]
+
+    # create inferencer and run
+    demo = inferencer(models, visualizer)
+    demo.run(args.input, args.loop and not args.no_show)
+
+
+if __name__ == "__main__":
+    sys.exit(main() or 0)

deployment/Detection task/python/model_wrappers/__init__.py

@@ -0,0 +1,15 @@
+"""Model Wrapper Initialization of OTX Detection."""
+
+# Copyright (C) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.

deployment/Detection task/python/requirements.txt

@@ -0,0 +1,4 @@
+openvino==2023.3.0
+openvino-model-api==0.1.9.1
+otx==1.6.5
+numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime

deployment/project.json

@@ -0,0 +1,71 @@
+{
+   "id": "6483613d18fb8c1c529cb05a",
+   "name": "birds",
+   "creation_time": "2023-06-09T17:28:29.944000+00:00",
+   "creator_id": "dd725a2c-b183-4616-bcf3-0894843fb6a5",
+   "pipeline": {
+      "tasks": [
+         {
+            "id": "6483613d18fb8c1c529cb05b",
+            "title": "Dataset",
+            "task_type": "dataset"
+         },
+         {
+            "id": "6483613d18fb8c1c529cb05e",
+            "title": "Detection task",
+            "task_type": "detection",
+            "labels": [
+               {
+                  "id": "6483613d18fb8c1c529cb061",
+                  "name": "bird",
+                  "is_anomalous": false,
+                  "color": "#ff0000ff",
+                  "hotkey": "",
+                  "is_empty": false,
+                  "group": "Default group",
+                  "parent_id": null
+               },
+               {
+                  "id": "6483613d18fb8c1c529cb065",
+                  "name": "No Object",
+                  "is_anomalous": false,
+                  "color": "#000000ff",
+                  "hotkey": "",
+                  "is_empty": true,
+                  "group": "No Object",
+                  "parent_id": null
+               }
+            ],
+            "label_schema_id": "6483613d18fb8c1c529cb067"
+         }
+      ],
+      "connections": [
+         {
+            "from": "6483613d18fb8c1c529cb05b",
+            "to": "6483613d18fb8c1c529cb05e"
+         }
+      ]
+   },
+   "thumbnail": "/api/v1/organizations/0ec46502-f590-4358-afff-a6beb25fe89f/workspaces/97ecb1e9-4367-4bc6-b335-1c6e7aedbf77/projects/6483613d18fb8c1c529cb05a/thumbnail",
+   "performance": {
+      "score": 0.9401041666666665,
+      "task_performances": [
+         {
+            "task_id": "6483613d18fb8c1c529cb05e",
+            "score": {
+               "value": 0.9401041666666665,
+               "metric_type": "f-measure"
+            }
+         }
+      ]
+   },
+   "storage_info": {},
+   "datasets": [
+      {
+         "id": "6483613d18fb8c1c529cb062",
+         "name": "Dataset",
+         "use_for_training": true,
+         "creation_time": "2023-06-09T17:28:29.944000+00:00"
+      }
+   ]
+}