--- language: - eng license: wtfpl tags: - multilabel-image-classification - multilabel - generated_from_trainer base_model: facebook/dinov2-large model-index: - name: DinoVdeau-large-2024_05_31-batch-size32_epochs150_freeze results: [] --- DinoVd'eau is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large). It achieves the following results on the test set: - Loss: 0.1235 - F1 Micro: 0.8217 - F1 Macro: 0.7173 - Roc Auc: 0.8829 - Accuracy: 0.3183 --- # 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](https://github.com/SeatizenDOI/DinoVdeau). - **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg) --- # 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 | 1488 | 465 | 455 | 2408 | | Acropore_digitised | 566 | 169 | 153 | 888 | | Acropore_sub_massive | 147 | 48 | 48 | 243 | | Acropore_tabular | 997 | 290 | 302 | 1589 | | Algae_assembly | 2537 | 859 | 842 | 4238 | | Algae_drawn_up | 368 | 121 | 131 | 620 | | Algae_limestone | 1651 | 559 | 562 | 2772 | | Algae_sodding | 3155 | 980 | 982 | 5117 | | Atra/Leucospilota | 1090 | 359 | 343 | 1792 | | Bleached_coral | 219 | 69 | 72 | 360 | | Blurred | 190 | 63 | 67 | 320 | | Dead_coral | 1981 | 644 | 639 | 3264 | | Fish | 2029 | 657 | 635 | 3321 | | Homo_sapiens | 160 | 63 | 59 | 282 | | Human_object | 156 | 61 | 53 | 270 | | Living_coral | 854 | 289 | 271 | 1414 | | Millepore | 383 | 129 | 125 | 637 | | No_acropore_encrusting | 420 | 153 | 152 | 725 | | No_acropore_foliaceous | 204 | 44 | 38 | 286 | | No_acropore_massive | 1017 | 345 | 343 | 1705 | | No_acropore_solitary | 195 | 54 | 54 | 303 | | No_acropore_sub_massive | 1383 | 445 | 428 | 2256 | | Rock | 4469 | 1499 | 1489 | 7457 | | Rubble | 3089 | 1011 | 1023 | 5123 | | Sand | 5840 | 1949 | 1930 | 9719 | | Sea_cucumber | 1413 | 445 | 436 | 2294 | | Sea_urchins | 327 | 107 | 111 | 545 | | Sponge | 269 | 104 | 97 | 470 | | Syringodium_isoetifolium | 1214 | 388 | 393 | 1995 | | Thalassodendron_ciliatum | 781 | 262 | 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 | 0.17266152799129486 | 0.23719491234101064 | 0.7471888818698673 | 0.51050515939363 | 0.001 2 | 0.15843337774276733 | 0.2506015812994156 | 0.759063829787234 | 0.5754792679279648 | 0.001 3 | 0.15126323699951172 | 0.24682021313166036 | 0.7712364371467471 | 0.5997966166279454 | 0.001 4 | 0.14971894025802612 | 0.2516328635269852 | 0.7649153278332611 | 0.6102972679327003 | 0.001 5 | 0.14786843955516815 | 0.26194568580268135 | 0.7743272938347361 | 0.6048101972148001 | 0.001 6 | 0.1464792788028717 | 0.23856995531110348 | 0.7744215397559949 | 0.6142379178757014 | 0.001 7 | 0.1471046507358551 | 0.25232038501203163 | 0.7745991019884542 | 0.6378919485445709 | 0.001 8 | 0.14884118735790253 | 0.2492265383293228 | 0.7730045646516785 | 0.6349994110674028 | 0.001 9 | 0.1696147322654724 | 0.17084908903403231 | 0.7571320373643019 | 0.5913962348409779 | 0.001 10 | 0.14972557127475739 | 0.2633207287727741 | 0.7752799457074991 | 0.6155340436417148 | 0.001 11 | 0.14687682688236237 | 0.2591955998624957 | 0.7746945972041475 | 0.6157336320155493 | 0.001 12 | 0.14556235074996948 | 0.25575799243726366 | 0.7830250450155508 | 0.6488857718028787 | 0.001 13 | 0.1428324580192566 | 0.2554142316947405 | 0.7843104596935376 | 0.6580664808969154 | 0.001 14 | 0.14352969825267792 | 0.2506015812994156 | 0.7831899072604227 | 0.6418882151192971 | 0.001 15 | 0.14535032212734222 | 0.25335166723960123 | 0.7810577597952191 | 0.6573171730380419 | 0.001 16 | 0.1448182314634323 | 0.26400825025782054 | 0.7865547601415288 | 0.6503641322273297 | 0.001 17 | 0.14306068420410156 | 0.2595393606050189 | 0.7819386012413047 | 0.6441131277200526 | 0.001 18 | 0.1446852684020996 | 0.25472671020969406 | 0.7838684089675614 | 0.6412796509721743 | 0.001 19 | 0.1414887011051178 | 0.26469577174286696 | 0.7836073910832345 | 0.6441719729073186 | 0.001 20 | 0.1405678391456604 | 0.2609144035751117 | 0.7838809251976828 | 0.6436752083994657 | 0.001 21 | 0.14136537909507751 | 0.25747679614987967 | 0.7847450484618627 | 0.6461144436259285 | 0.001 22 | 0.1386082023382187 | 0.26194568580268135 | 0.78838833814364 | 0.6587430664519773 | 0.001 23 | 0.13822348415851593 | 0.268133379168099 | 0.7900436534586972 | 0.6502835561459052 | 0.001 24 | 0.13893215358257294 | 0.2650395324853902 | 0.7880997276346112 | 0.6519157870757598 | 0.001 25 | 0.1396929770708084 | 0.2695084221381918 | 0.7883266848624816 | 0.6553890148296274 | 0.001 26 | 0.14012907445430756 | 0.2650395324853902 | 0.7793733121865489 | 0.6349973216617472 | 0.001 27 | 0.1402139514684677 | 0.2499140598143692 | 0.7915316128502852 | 0.6667665130743651 | 0.001 28 | 0.1389472633600235 | 0.26400825025782054 | 0.7907676869041647 | 0.6575889205208383 | 0.001 29 | 0.14010308682918549 | 0.268133379168099 | 0.793982620101656 | 0.6657248014277785 | 0.001 30 | 0.13457615673542023 | 0.2739773117909935 | 0.7986502613061192 | 0.6710788331447379 | 0.0001 31 | 0.1323619782924652 | 0.2784462014437951 | 0.7977876476996564 | 0.6773801465956272 | 0.0001 32 | 0.13328427076339722 | 0.27294602956342384 | 0.8018979833926453 | 0.6778747090268944 | 0.0001 33 | 0.13171622157096863 | 0.2794774836713647 | 0.8033507506013103 | 0.6849041252254977 | 0.0001 34 | 0.1307307630777359 | 0.27982124441388795 | 0.8020612558700079 | 0.6836107081350358 | 0.0001 35 | 0.13025963306427002 | 0.2853214162942592 | 0.8046306144154465 | 0.6903143910655651 | 0.0001 36 | 0.1296597272157669 | 0.290134066689584 | 0.8050928824879983 | 0.6931792274102746 | 0.0001 37 | 0.12961770594120026 | 0.288415262976968 | 0.8034535718733136 | 0.6884731439645044 | 0.0001 38 | 0.12927678227424622 | 0.28704022000687524 | 0.8042539049518111 | 0.68878596648098 | 0.0001 39 | 0.13043531775474548 | 0.2877277414919216 | 0.8032656478961692 | 0.6869571453655325 | 0.0001 40 | 0.12887024879455566 | 0.29082158817463044 | 0.8063839414256921 | 0.693512791025367 | 0.0001 41 | 0.12975196540355682 | 0.28704022000687524 | 0.8037148594377511 | 0.6924207618698955 | 0.0001 42 | 0.12854912877082825 | 0.29597799931247853 | 0.8086140163056905 | 0.6899593151302184 | 0.0001 43 | 0.12848526239395142 | 0.28704022000687524 | 0.8066790352504639 | 0.6897522012507461 | 0.0001 44 | 0.12860073149204254 | 0.2921966311447233 | 0.8077876984126985 | 0.6930773017154849 | 0.0001 45 | 0.12759028375148773 | 0.2956342385699553 | 0.8106891471599279 | 0.7036740023156066 | 0.0001 46 | 0.12775476276874542 | 0.2956342385699553 | 0.8078483318155477 | 0.6950811210030032 | 0.0001 47 | 0.12771955132484436 | 0.2918528704022001 | 0.811402081977879 | 0.7062619936159387 | 0.0001 48 | 0.12763886153697968 | 0.29082158817463044 | 0.8052384150436536 | 0.6955184040970307 | 0.0001 49 | 0.12784114480018616 | 0.288415262976968 | 0.8098617549329287 | 0.7020626151896329 | 0.0001 50 | 0.12698890268802643 | 0.2939154348573393 | 0.8101997029212742 | 0.705975464457343 | 0.0001 51 | 0.12740205228328705 | 0.29769680302509455 | 0.8129121550109908 | 0.709798905916108 | 0.0001 52 | 0.12743453681468964 | 0.2918528704022001 | 0.8121578560339897 | 0.707621451538995 | 0.0001 53 | 0.12782631814479828 | 0.29322791337229287 | 0.8083623693379792 | 0.694158242732084 | 0.0001 54 | 0.12641482055187225 | 0.2939154348573393 | 0.8095710389288371 | 0.6945407396863954 | 0.0001 55 | 0.12613853812217712 | 0.29838432451014096 | 0.8117057825241112 | 0.711364278725223 | 0.0001 56 | 0.12689372897148132 | 0.2990718459951873 | 0.8113456464379947 | 0.7063129571309116 | 0.0001 57 | 0.12595032155513763 | 0.2990718459951873 | 0.8135370461639561 | 0.7092631560861781 | 0.0001 58 | 0.12691068649291992 | 0.29254039188724645 | 0.8129296235679215 | 0.7038026958133716 | 0.0001 59 | 0.1266162097454071 | 0.2921966311447233 | 0.8112015199702616 | 0.7074106748802989 | 0.0001 60 | 0.1263982653617859 | 0.29322791337229287 | 0.8090558527179996 | 0.6985455534303677 | 0.0001 61 | 0.12581084668636322 | 0.29597799931247853 | 0.8097471110366773 | 0.7041363825305129 | 0.0001 62 | 0.12699832022190094 | 0.2911653489171537 | 0.8132532581607222 | 0.7066021008547532 | 0.0001 63 | 0.12574061751365662 | 0.29700928154004813 | 0.8111204013377926 | 0.7059955812986827 | 0.0001 64 | 0.1252606362104416 | 0.2939154348573393 | 0.8139229062217472 | 0.7090348173219025 | 0.0001 65 | 0.12513236701488495 | 0.2963217600550017 | 0.8126994653292992 | 0.70468515245698 | 0.0001 66 | 0.125584214925766 | 0.2949467170849089 | 0.8140287622403409 | 0.7118614262032313 | 0.0001 67 | 0.12539814412593842 | 0.2939154348573393 | 0.8111171298804116 | 0.7062384835160513 | 0.0001 68 | 0.12564098834991455 | 0.29597799931247853 | 0.8151627792982313 | 0.7112630392285837 | 0.0001 69 | 0.12584172189235687 | 0.29941560673771056 | 0.8142985980159057 | 0.7140636849121678 | 0.0001 70 | 0.12478043138980865 | 0.29838432451014096 | 0.8161761696205642 | 0.718410533722422 | 0.0001 71 | 0.12550216913223267 | 0.2963217600550017 | 0.8134349886668041 | 0.7097253169772126 | 0.0001 72 | 0.12681567668914795 | 0.2980405637676177 | 0.8132986082851795 | 0.7066535688303756 | 0.0001 73 | 0.12942491471767426 | 0.30044688896528016 | 0.8154191311441974 | 0.720052479680047 | 0.0001 74 | 0.1252431720495224 | 0.29597799931247853 | 0.8141104799538981 | 0.7156506533830808 | 0.0001 75 | 0.12525109946727753 | 0.29975936748023374 | 0.8164923076923079 | 0.7162358218470065 | 0.0001 76 | 0.125084787607193 | 0.2980405637676177 | 0.8126263668248401 | 0.7050812600051921 | 0.0001 77 | 0.12381099909543991 | 0.301821931935373 | 0.8169637369391518 | 0.7198797063603358 | 1e-05 78 | 0.12425024807453156 | 0.29975936748023374 | 0.8142230317079229 | 0.7125350103590883 | 1e-05 79 | 0.12345358729362488 | 0.3028532141629426 | 0.8181743958197256 | 0.721102526527663 | 1e-05 80 | 0.12395191192626953 | 0.30216569267789617 | 0.8199023445381542 | 0.7272188807325698 | 1e-05 81 | 0.12421117722988129 | 0.3011344104503266 | 0.816331575477917 | 0.7213714212722541 | 1e-05 82 | 0.1238287091255188 | 0.3042282571330354 | 0.8184078588024294 | 0.7220641511602056 | 1e-05 83 | 0.1235181912779808 | 0.301821931935373 | 0.8180256808702052 | 0.7213144754619842 | 1e-05 84 | 0.12367285788059235 | 0.3025094534204194 | 0.8176539851394697 | 0.7232957874603048 | 1e-05 85 | 0.12321745604276657 | 0.3045720178755586 | 0.8164039937288555 | 0.7142044524891776 | 1e-05 86 | 0.1237027570605278 | 0.303540735647989 | 0.8165334212478365 | 0.724290897047381 | 1e-05 87 | 0.12352145463228226 | 0.30216569267789617 | 0.8180700172173485 | 0.7200937974757147 | 1e-05 88 | 0.12393338233232498 | 0.303540735647989 | 0.8187826933214387 | 0.7208874018784168 | 1e-05 89 | 0.12368057668209076 | 0.3028532141629426 | 0.8175708900180297 | 0.717195490689832 | 1e-05 90 | 0.12319833785295486 | 0.30594706084565143 | 0.8169988469774335 | 0.7202869112854988 | 1e-05 91 | 0.12350083887577057 | 0.303540735647989 | 0.8199162022535897 | 0.7243798285402773 | 1e-05 92 | 0.12410824745893478 | 0.30560330010312825 | 0.820052770448549 | 0.7264331892084873 | 1e-05 93 | 0.12333476543426514 | 0.3028532141629426 | 0.8191851972082453 | 0.7265708763385225 | 1e-05 94 | 0.12396726757287979 | 0.29769680302509455 | 0.8147507922788823 | 0.7139339929197925 | 1e-05 95 | 0.12366786599159241 | 0.2980405637676177 | 0.8202541859995964 | 0.7272882506355968 | 1e-05 96 | 0.12370884418487549 | 0.30491577861808183 | 0.8192505510653931 | 0.7202747551284554 | 1e-05 97 | 0.12347108125686646 | 0.3007906497078034 | 0.8185695138296577 | 0.7212605213289621 | 1.0000000000000002e-06 98 | 0.12309076637029648 | 0.3038844963905122 | 0.8167741405511973 | 0.7173148781861652 | 1.0000000000000002e-06 99 | 0.12337860465049744 | 0.30216569267789617 | 0.8195458231954581 | 0.7265958923493796 | 1.0000000000000002e-06 100 | 0.12339676916599274 | 0.3025094534204194 | 0.8176836250613447 | 0.721153078194628 | 1.0000000000000002e-06 101 | 0.12300820648670197 | 0.3007906497078034 | 0.8195522327414572 | 0.727514273407078 | 1.0000000000000002e-06 102 | 0.12340469658374786 | 0.30147817119284975 | 0.8154413898909484 | 0.7137375786593034 | 1.0000000000000002e-06 103 | 0.12386961281299591 | 0.29941560673771056 | 0.8163972286374134 | 0.7145427089697738 | 1.0000000000000002e-06 104 | 0.1235337182879448 | 0.30147817119284975 | 0.8170896715732502 | 0.7188862048208717 | 1.0000000000000002e-06 105 | 0.12342803180217743 | 0.301821931935373 | 0.8191040415516962 | 0.7225140266508299 | 1.0000000000000002e-06 106 | 0.12395947426557541 | 0.30216569267789617 | 0.8179164977705716 | 0.7182648261775325 | 1.0000000000000002e-06 107 | 0.1234135553240776 | 0.30147817119284975 | 0.8188311688311688 | 0.7269935921863244 | 1.0000000000000002e-06 108 | 0.12344102561473846 | 0.29975936748023374 | 0.8162340337865678 | 0.722601561126108 | 1.0000000000000002e-07 109 | 0.12361280620098114 | 0.303540735647989 | 0.8215820979470492 | 0.7257459935415149 | 1.0000000000000002e-07 110 | 0.12370219826698303 | 0.3031969749054658 | 0.8204050284975141 | 0.7232171570839454 | 1.0000000000000002e-07 111 | 0.12323758751153946 | 0.305259539360605 | 0.8178334500803495 | 0.7223581742811798 | 1.0000000000000002e-07 --- # CO2 Emissions The estimated CO2 emissions for training this model are documented below: - **Emissions**: 1.717335003598466 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