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DinoVdeau-small-2024_08_31-batch-size32_epochs150_freeze

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metadata
language:
  - eng
license: wtfpl
tags:
  - multilabel-image-classification
  - multilabel
  - generated_from_trainer
base_model: facebook/dinov2-small
model-index:
  - name: DinoVdeau-small-2024_08_31-batch-size32_epochs150_freeze
    results: []

DinoVd'eau is a fine-tuned version of facebook/dinov2-small. It achieves the following results on the test set:

  • Loss: 0.1320
  • F1 Micro: 0.8009
  • F1 Macro: 0.6614
  • Roc Auc: 0.8649
  • Accuracy: 0.2903

Model description

DinoVd'eau is a model built on top of dinov2 model for underwater multilabel image classification.The classification head is a combination of linear, ReLU, batch normalization, and dropout layers.

The source code for training the model can be found in this Git repository.

Intended uses & limitations

You can use the raw model for classify diverse marine species, encompassing coral morphotypes classes taken from the Global Coral Reef Monitoring Network (GCRMN), habitats classes and seagrass species.

Training and evaluation data

Details on the number of images for each class are given in the following table:

Class train val test Total
Acropore_branched 1469 464 475 2408
Acropore_digitised 568 160 160 888
Acropore_sub_massive 150 50 43 243
Acropore_tabular 999 297 293 1589
Algae_assembly 2546 847 845 4238
Algae_drawn_up 367 126 127 620
Algae_limestone 1652 557 563 2772
Algae_sodding 3148 984 985 5117
Atra/Leucospilota 1084 348 360 1792
Bleached_coral 219 71 70 360
Blurred 191 67 62 320
Dead_coral 1979 642 643 3264
Fish 2018 656 647 3321
Homo_sapiens 161 62 59 282
Human_object 157 58 55 270
Living_coral 406 154 141 701
Millepore 385 127 125 637
No_acropore_encrusting 441 130 154 725
No_acropore_foliaceous 204 36 46 286
No_acropore_massive 1031 336 338 1705
No_acropore_solitary 202 53 48 303
No_acropore_sub_massive 1401 433 422 2256
Rock 4489 1495 1473 7457
Rubble 3092 1030 1001 5123
Sand 5842 1939 1938 9719
Sea_cucumber 1408 439 447 2294
Sea_urchins 327 107 111 545
Sponge 269 96 105 470
Syringodium_isoetifolium 1212 392 391 1995
Thalassodendron_ciliatum 782 261 260 1303
Useless 579 193 193 965

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • Number of Epochs: 150
  • Learning Rate: 0.001
  • Train Batch Size: 32
  • Eval Batch Size: 32
  • Optimizer: Adam
  • LR Scheduler Type: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
  • Freeze Encoder: Yes
  • Data Augmentation: Yes

Data Augmentation

Data were augmented using the following transformations :

Train Transforms

  • PreProcess: No additional parameters
  • Resize: probability=1.00
  • RandomHorizontalFlip: probability=0.25
  • RandomVerticalFlip: probability=0.25
  • ColorJiggle: probability=0.25
  • RandomPerspective: probability=0.25
  • Normalize: probability=1.00

Val Transforms

  • PreProcess: No additional parameters
  • Resize: probability=1.00
  • Normalize: probability=1.00

Training results

Epoch Validation Loss Accuracy F1 Macro F1 Micro Learning Rate
1 0.19568666815757751 0.19057519057519057 0.7088941673264713 0.4058921954514261 0.001
2 0.17198018729686737 0.21933471933471935 0.738139514768845 0.4867943512801917 0.001
3 0.16209888458251953 0.23215523215523215 0.7578947368421052 0.5587016500092944 0.001
4 0.15948981046676636 0.22487872487872487 0.7463059684835497 0.5561953540051209 0.001
5 0.15691693127155304 0.23146223146223147 0.7510718113612004 0.5723046956548954 0.001
6 0.15302371978759766 0.2363132363132363 0.7634727923836142 0.5786669115862841 0.001
7 0.1523299366235733 0.23354123354123354 0.7651630269613162 0.5981729145672101 0.001
8 0.15311872959136963 0.24185724185724186 0.7655172413793103 0.587992292024695 0.001
9 0.14992575347423553 0.24012474012474014 0.7699542669773061 0.606908576330327 0.001
10 0.1509619951248169 0.24393624393624394 0.7606115107913669 0.5829080312220596 0.001
11 0.1520717293024063 0.2505197505197505 0.7689559002963221 0.5976223089766404 0.001
12 0.15027731657028198 0.2442827442827443 0.7759986516096409 0.607405900640871 0.001
13 0.1504218876361847 0.24393624393624394 0.7623558852444365 0.6003271512523337 0.001
14 0.1496724784374237 0.24462924462924462 0.7644358114073813 0.602811285040826 0.001
15 0.14749661087989807 0.2512127512127512 0.7751615281210703 0.6066013767027806 0.001
16 0.14998775720596313 0.24636174636174638 0.7645565108923241 0.5838354990739413 0.001
17 0.15297245979309082 0.24566874566874566 0.7719883641341547 0.6073459016890155 0.001
18 0.14907290041446686 0.24393624393624394 0.7751951282271207 0.614324753279198 0.001
19 0.14951026439666748 0.23458073458073458 0.7739734788726388 0.6075499214740471 0.001
20 0.14873762428760529 0.24532224532224534 0.7636993911381718 0.595638442008225 0.001
21 0.14705629646778107 0.24740124740124741 0.780452718426063 0.6164990545073296 0.001
22 0.1508719027042389 0.24532224532224534 0.7753641707130079 0.6073576225776433 0.001
23 0.15015815198421478 0.2428967428967429 0.771920553133395 0.6127152502703448 0.001
24 0.14965225756168365 0.24012474012474014 0.7698941591532732 0.5849380548549015 0.001
25 0.14702074229717255 0.24255024255024255 0.7761348897535668 0.6035289549510865 0.001
26 0.14808295667171478 0.24220374220374222 0.7751430907604253 0.6064603919289959 0.001
27 0.14581289887428284 0.24740124740124741 0.7689308343302761 0.6135774018658996 0.001
28 0.1453842669725418 0.24462924462924462 0.7751325049960902 0.6077297645661711 0.001
29 0.14941243827342987 0.24566874566874566 0.7735191637630662 0.6107922701154117 0.001
30 0.14549985527992249 0.24982674982674982 0.7705324709843182 0.5982833860845571 0.001
31 0.14541107416152954 0.2532917532917533 0.7784728768532008 0.6068619458731248 0.001
32 0.14657220244407654 0.24532224532224534 0.7746102833519939 0.6145316287096297 0.001
33 0.14459234476089478 0.253984753984754 0.777031154551008 0.6124691593400795 0.001
34 0.1468168944120407 0.24462924462924462 0.7781283769180896 0.6168054796129936 0.001
35 0.14858707785606384 0.2494802494802495 0.7766880749869814 0.6193343400891848 0.001
36 0.14637114107608795 0.24878724878724878 0.7718835224773468 0.6092667253949349 0.001
37 0.1448281705379486 0.24982674982674982 0.7733602776435442 0.6127183895875491 0.001
38 0.1450735628604889 0.25225225225225223 0.7814896880859042 0.6109962510638844 0.001
39 0.14469724893569946 0.24982674982674982 0.7824146207942057 0.6272196317832909 0.001
40 0.14824891090393066 0.25363825363825365 0.7836651178652115 0.6265963634718456 0.0001
41 0.14141727983951569 0.2616077616077616 0.7833456473553827 0.6323784470247855 0.0001
42 0.13979895412921906 0.26195426195426197 0.7884351407000686 0.6371841233046203 0.0001
43 0.14107641577720642 0.26403326403326405 0.7871061893724783 0.6366820358518588 0.0001
44 0.13898694515228271 0.26126126126126126 0.787878787878788 0.6256922069455233 0.0001
45 0.13859130442142487 0.2664587664587665 0.7894011202068074 0.6421056073559387 0.0001
46 0.139601469039917 0.2664587664587665 0.7873893327575039 0.6283048537279357 0.0001
47 0.13869330286979675 0.2636867636867637 0.7863567238757333 0.6286555138094179 0.0001
48 0.13777127861976624 0.26784476784476785 0.7913177234660741 0.6334934953582803 0.0001
49 0.1377096027135849 0.26403326403326405 0.7933989479042932 0.6381777921693204 0.0001
50 0.13755330443382263 0.2674982674982675 0.7918342891380639 0.6362718007605523 0.0001
51 0.13754987716674805 0.2661122661122661 0.7928808087673094 0.6426825970872383 0.0001
52 0.13771678507328033 0.26576576576576577 0.7871186146434616 0.6367912909960436 0.0001
53 0.13740690052509308 0.2692307692307692 0.7928592630284527 0.640555047060403 0.0001
54 0.1368684023618698 0.27165627165627165 0.7920979171140219 0.6412320555565514 0.0001
55 0.13703426718711853 0.2702702702702703 0.7914089347079037 0.6377616721633446 0.0001
56 0.1364637017250061 0.2643797643797644 0.7931107623128156 0.6425003998141597 0.0001
57 0.13675515353679657 0.2674982674982675 0.7926408585665006 0.6381793578718891 0.0001
58 0.1364695280790329 0.2674982674982675 0.791562634524322 0.637380953089336 0.0001
59 0.13641765713691711 0.2674982674982675 0.7922245108135942 0.6428884521567982 0.0001
60 0.13687649369239807 0.26507276507276506 0.7882888744307093 0.6357999016219877 0.0001
61 0.13638463616371155 0.2713097713097713 0.7945638702508654 0.6503848519713329 0.0001
62 0.13563227653503418 0.2751212751212751 0.7931640039405492 0.6441767594174573 0.0001
63 0.1355270892381668 0.27373527373527373 0.7966116124638174 0.6515952055035917 0.0001
64 0.13592010736465454 0.26784476784476785 0.7934075342465754 0.6450040026439422 0.0001
65 0.13569533824920654 0.27061677061677064 0.7936467053015668 0.64551501310817 0.0001
66 0.13565082848072052 0.2713097713097713 0.794643237940888 0.6477176853690674 0.0001
67 0.13533934950828552 0.27546777546777546 0.7965922095536813 0.6544361257862924 0.0001
68 0.1353396475315094 0.2733887733887734 0.7955772910907932 0.6519486064773884 0.0001
69 0.13474246859550476 0.26992376992376993 0.7966188524590164 0.6515714856354324 0.0001
70 0.13504748046398163 0.272002772002772 0.7944687795241776 0.6441608871918139 0.0001
71 0.13502468168735504 0.27234927234927236 0.7933057280883367 0.6441889860402124 0.0001
72 0.1344645917415619 0.2758142758142758 0.7969950486597234 0.6484748365424647 0.0001
73 0.1341526359319687 0.27616077616077617 0.7977006599957419 0.6518769914193778 0.0001
74 0.13499116897583008 0.2751212751212751 0.7914797229603171 0.641334935505441 0.0001
75 0.13461369276046753 0.2751212751212751 0.7946678133734681 0.6485229770180625 0.0001
76 0.13438266515731812 0.2758142758142758 0.7964594201659113 0.6478195810395848 0.0001
77 0.13460540771484375 0.27754677754677753 0.7977742853502102 0.6536737916153181 0.0001
78 0.13411369919776917 0.27754677754677753 0.7978169818504888 0.6543115985953537 0.0001
79 0.13399606943130493 0.2740817740817741 0.7953020134228188 0.6523004018612216 0.0001
80 0.1344238668680191 0.27823977823977825 0.7993085420355848 0.6545582038870168 0.0001
81 0.13405664265155792 0.2758142758142758 0.7966715529878418 0.6559691700651434 0.0001
82 0.13407430052757263 0.2765072765072765 0.7947541551246537 0.6453669674995801 0.0001
83 0.1350804716348648 0.2702702702702703 0.7924365020985678 0.645966570658811 0.0001
84 0.13387472927570343 0.27546777546777546 0.7957293542577825 0.6512285101875886 0.0001
85 0.13341927528381348 0.27927927927927926 0.7990622335890879 0.6531817491521362 0.0001
86 0.13337253034114838 0.2747747747747748 0.7988261313371896 0.6595866427349153 0.0001
87 0.1339845359325409 0.27442827442827444 0.7956179390619651 0.6467323251879672 0.0001
88 0.13357459008693695 0.2747747747747748 0.7981612326551459 0.648318545746826 0.0001
89 0.13366733491420746 0.2806652806652807 0.8014968675104065 0.6585340844298272 0.0001
90 0.1332736760377884 0.2772002772002772 0.8010798042854732 0.66211749340029 0.0001
91 0.13367226719856262 0.27823977823977825 0.7956933454403943 0.6528573832362276 0.0001
92 0.13348612189292908 0.27546777546777546 0.796086375587259 0.6513649424471982 0.0001
93 0.1330718696117401 0.2758142758142758 0.8001861094662043 0.6559763883082907 0.0001
94 0.13329002261161804 0.2758142758142758 0.7995090362720617 0.6553585917255438 0.0001
95 0.13314621150493622 0.2758142758142758 0.7979651162790697 0.6579543710907207 0.0001
96 0.13279949128627777 0.2751212751212751 0.7992523999660183 0.6556445954379041 0.0001
97 0.1332886964082718 0.27823977823977825 0.7977296181630549 0.6492741904723621 0.0001
98 0.13266970217227936 0.27546777546777546 0.799611141637432 0.6600105762308898 0.0001
99 0.13253149390220642 0.27165627165627165 0.7978809757764771 0.6589970862385839 0.0001
100 0.1329408884048462 0.27616077616077617 0.797143840330351 0.6570195655430786 0.0001
101 0.13274870812892914 0.28205128205128205 0.7991615690636095 0.657951499975745 0.0001
102 0.1326293796300888 0.2817047817047817 0.7986821274228745 0.654306822863844 0.0001
103 0.13247379660606384 0.2803187803187803 0.7993688968487486 0.6518495856500403 0.0001
104 0.13315415382385254 0.27754677754677753 0.8010850676047981 0.6612536009112525 0.0001
105 0.13218620419502258 0.2830907830907831 0.8012698412698412 0.6635718544409769 0.0001
106 0.13239973783493042 0.2830907830907831 0.800988243312319 0.6588128942023547 0.0001
107 0.13358280062675476 0.2785862785862786 0.7985513421389007 0.650564106362156 0.0001
108 0.13270235061645508 0.2796257796257796 0.7995554225623049 0.6501303094783896 0.0001
109 0.1318453699350357 0.2806652806652807 0.8000342553738118 0.6579556871315007 0.0001
110 0.13255637884140015 0.2803187803187803 0.7997274043785672 0.6582487839550253 0.0001
111 0.1319260448217392 0.2785862785862786 0.8012935069355799 0.6608614747058748 0.0001
112 0.13223350048065186 0.28101178101178104 0.8019278738426415 0.6595016342799644 0.0001
113 0.13213913142681122 0.27997227997227997 0.8024988392216453 0.6592029124671744 0.0001
114 0.13204564154148102 0.2823977823977824 0.8025030654094965 0.663088095209859 0.0001
115 0.1319342404603958 0.28378378378378377 0.8004266211604096 0.659797224924612 0.0001
116 0.13186337053775787 0.2844767844767845 0.8022295974810655 0.6627361818946377 1e-05
117 0.1317850947380066 0.28205128205128205 0.8012607547491268 0.6604165936303265 1e-05
118 0.13159342110157013 0.2796257796257796 0.8002395926924228 0.6590147410119703 1e-05
119 0.1319129317998886 0.28274428274428276 0.8036745185622182 0.6608406822787987 1e-05
120 0.13164088129997253 0.28135828135828134 0.803593372600534 0.6614581971670047 1e-05
121 0.13184630870819092 0.28101178101178104 0.8012604863092451 0.6610641151618838 1e-05
122 0.13215216994285583 0.2817047817047817 0.8049611099432415 0.6647378818356079 1e-05
123 0.13187836110591888 0.2817047817047817 0.8010107932156931 0.6604978306251739 1e-05
124 0.13141389191150665 0.2806652806652807 0.8018739352640545 0.6621515776947642 1e-05
125 0.13139639794826508 0.2862092862092862 0.804345987993574 0.6640721616133445 1e-05
126 0.13103623688220978 0.2862092862092862 0.804212663367593 0.663003919720051 1e-05
127 0.13152988255023956 0.28586278586278585 0.8038346213944846 0.6597731906072118 1e-05
128 0.13113313913345337 0.2869022869022869 0.8042412977357216 0.668197478893632 1e-05
129 0.13096605241298676 0.28274428274428276 0.8034694309287074 0.6652814888251478 1e-05
130 0.1310083270072937 0.28655578655578656 0.8034491503931017 0.6657375892895663 1e-05
131 0.13133247196674347 0.2834372834372834 0.8052362171687506 0.6709132204127336 1e-05
132 0.13149647414684296 0.2806652806652807 0.7985562048814026 0.6557913726655867 1e-05
133 0.1311328113079071 0.28794178794178793 0.8051816958277256 0.6689392948255155 1e-05
134 0.1308571696281433 0.28274428274428276 0.802060714437774 0.6648386499372343 1e-05
135 0.13148072361946106 0.2869022869022869 0.8038277511961722 0.6684163123065296 1e-05
136 0.13150115311145782 0.28274428274428276 0.8024591213764248 0.659009971789042 1e-05
137 0.1310679018497467 0.28586278586278585 0.8035592643051771 0.6666808903899752 1e-05
138 0.13124705851078033 0.2844767844767845 0.8035426731078905 0.6665598962110765 1e-05
139 0.13104070723056793 0.28967428967428965 0.8052538519828238 0.6661043989752415 1e-05
140 0.13169734179973602 0.2834372834372834 0.8020416843896214 0.663466069531375 1e-05
141 0.13089434802532196 0.2875952875952876 0.8046521463311481 0.6687691213000826 1.0000000000000002e-06
142 0.13103386759757996 0.28586278586278585 0.8041640110473762 0.6642894279153319 1.0000000000000002e-06
143 0.13144278526306152 0.2872487872487873 0.8019270122783083 0.6623287859816251 1.0000000000000002e-06
144 0.1311902105808258 0.28378378378378377 0.8024974515800204 0.6647534218687892 1.0000000000000002e-06

CO2 Emissions

The estimated CO2 emissions for training this model are documented below:

  • Emissions: 1.6281850650290761 grams of CO2
  • Source: Code Carbon
  • Training Type: fine-tuning
  • Geographical Location: Brest, France
  • Hardware Used: NVIDIA Tesla V100 PCIe 32 Go

Framework Versions

  • Transformers: 4.41.1
  • Pytorch: 2.3.0+cu121
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1