--- language: - eng license: wtfpl tags: - multilabel-image-classification - multilabel - generated_from_trainer base_model: facebook/dinov2-large model-index: - name: DinoVdeau_Aina-large-2024_06_12-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: 0.1378 - F1 Micro: 0.8118 - F1 Macro: 0.5888 - Roc Auc: 0.8738 - Accuracy: 0.5906 --- # 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 | |:--------|--------:|------:|-------:|--------:| | Acr | 509 | 170 | 170 | 849 | | Ech | 149 | 55 | 49 | 253 | | Gal | 149 | 49 | 52 | 250 | | Mtp | 278 | 93 | 92 | 463 | | Poc | 166 | 54 | 60 | 280 | | Por | 265 | 88 | 88 | 441 | | ALGAE | 1221 | 407 | 407 | 2035 | | RDC | 185 | 65 | 69 | 319 | | SG | 1388 | 463 | 462 | 2313 | | P | 198 | 66 | 66 | 330 | | R | 1106 | 368 | 369 | 1843 | | S | 2178 | 726 | 726 | 3630 | | UNK | 132 | 44 | 44 | 220 | --- # 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 | 0.25564512610435486 | 0.5342987804878049 | 0.7703523693803159 | 0.42604577042963465 | 0.001 2 | 0.1855568140745163 | 0.5449695121951219 | 0.7607875994972768 | 0.40573815087342935 | 0.001 3 | 0.15800759196281433 | 0.5335365853658537 | 0.7772908366533864 | 0.47696929852236075 | 0.001 4 | 0.15843382477760315 | 0.5274390243902439 | 0.7700247729149464 | 0.39442465091480916 | 0.001 5 | 0.15615205466747284 | 0.5442073170731707 | 0.761375774407178 | 0.3747774830573479 | 0.001 6 | 0.1484147012233734 | 0.5548780487804879 | 0.7822349570200573 | 0.4447304719957427 | 0.001 7 | 0.1444726288318634 | 0.5647865853658537 | 0.7904456041750301 | 0.5284590192832462 | 0.001 8 | 0.14579781889915466 | 0.5487804878048781 | 0.7778469197261979 | 0.48048557941722936 | 0.001 9 | 0.1419043242931366 | 0.5655487804878049 | 0.7989700930877401 | 0.4775190765596756 | 0.001 10 | 0.14263293147087097 | 0.5663109756097561 | 0.7916750858759345 | 0.5507171237721333 | 0.001 11 | 0.1459268480539322 | 0.555640243902439 | 0.7765845441145505 | 0.43142510371678144 | 0.001 12 | 0.14237171411514282 | 0.5625 | 0.7899022801302932 | 0.47044459599035204 | 0.001 13 | 0.14195148646831512 | 0.5586890243902439 | 0.7957159857199523 | 0.5304167061533165 | 0.001 14 | 0.14145776629447937 | 0.5685975609756098 | 0.795869737887212 | 0.5302959882181708 | 0.001 15 | 0.14466659724712372 | 0.5746951219512195 | 0.7903159622486664 | 0.5020556893881151 | 0.001 16 | 0.15050330758094788 | 0.5548780487804879 | 0.7779618889809444 | 0.44075758480309546 | 0.001 17 | 0.15037894248962402 | 0.5625 | 0.7849117174959872 | 0.5071297581342885 | 0.001 18 | 0.15835434198379517 | 0.5632621951219512 | 0.7868521879411171 | 0.4937774793717716 | 0.001 19 | 0.13946771621704102 | 0.5678353658536586 | 0.795441147573197 | 0.52511737039556 | 0.001 20 | 0.1404852569103241 | 0.5678353658536586 | 0.8031007751937984 | 0.5902607630639873 | 0.001 21 | 0.14341644942760468 | 0.5640243902439024 | 0.7965933848286789 | 0.4816903721736541 | 0.001 22 | 0.14662735164165497 | 0.5510670731707317 | 0.7923046721633294 | 0.5288404087153705 | 0.001 23 | 0.14562036097049713 | 0.5746951219512195 | 0.7918968692449356 | 0.4974177137762122 | 0.001 24 | 0.13980671763420105 | 0.5586890243902439 | 0.7888934258881176 | 0.5008214078450646 | 0.001 25 | 0.13920389115810394 | 0.5807926829268293 | 0.8018232263178755 | 0.5881062115996991 | 0.001 26 | 0.14581459760665894 | 0.5846036585365854 | 0.8024120603015076 | 0.5378532463138629 | 0.001 27 | 0.1388114094734192 | 0.5716463414634146 | 0.7989535117729925 | 0.530975959272302 | 0.001 28 | 0.14750176668167114 | 0.5647865853658537 | 0.7952771662997797 | 0.4926175534546719 | 0.001 29 | 0.14280082285404205 | 0.5754573170731707 | 0.7916238965304866 | 0.4730643714031951 | 0.001 30 | 0.14457112550735474 | 0.5647865853658537 | 0.7960474308300396 | 0.5269721557360777 | 0.001 31 | 0.2518298327922821 | 0.555640243902439 | 0.7859719438877755 | 0.5163333467519122 | 0.001 32 | 0.13625293970108032 | 0.573170731707317 | 0.7984836392657622 | 0.5223572225800281 | 0.001 33 | 0.14134813845157623 | 0.583079268292683 | 0.797995991983968 | 0.5094906036573911 | 0.001 34 | 0.13918223977088928 | 0.5617378048780488 | 0.7939271255060729 | 0.539855430008089 | 0.001 35 | 0.14003774523735046 | 0.5876524390243902 | 0.8020833333333333 | 0.48732799827507844 | 0.001 36 | 0.15099692344665527 | 0.5853658536585366 | 0.8016096579476861 | 0.514951340134722 | 0.001 37 | 0.14428909122943878 | 0.5571646341463414 | 0.7900541407659917 | 0.48501044595638915 | 0.001 38 | 0.14405055344104767 | 0.5708841463414634 | 0.7945869521308826 | 0.5062759605333714 | 0.001 39 | 0.13543353974819183 | 0.5891768292682927 | 0.8024193548387097 | 0.535367946456459 | 0.0001 40 | 0.1358059197664261 | 0.59375 | 0.8035498184751916 | 0.5341458086147945 | 0.0001 41 | 0.13504844903945923 | 0.5945121951219512 | 0.8055001992825829 | 0.5376190537551451 | 0.0001 42 | 0.13691695034503937 | 0.5891768292682927 | 0.8035073734555599 | 0.5479834256786992 | 0.0001 43 | 0.13574542105197906 | 0.586890243902439 | 0.8031840796019901 | 0.5561858306297028 | 0.0001 44 | 0.13488240540027618 | 0.5891768292682927 | 0.8039920159680638 | 0.5433240630212841 | 0.0001 45 | 0.1361350268125534 | 0.5815548780487805 | 0.8024593415311385 | 0.5564064426334538 | 0.0001 46 | 0.1349516659975052 | 0.59375 | 0.8072669826224328 | 0.5738450359977211 | 0.0001 47 | 0.13875022530555725 | 0.586890243902439 | 0.8035892323030908 | 0.5398873319319681 | 0.0001 48 | 0.1370573788881302 | 0.586890243902439 | 0.8029458598726115 | 0.5475297928828294 | 0.0001 49 | 0.13690504431724548 | 0.5769817073170732 | 0.800396432111001 | 0.5558247567089973 | 0.0001 50 | 0.13561294972896576 | 0.5853658536585366 | 0.8055390702274975 | 0.5774903390899776 | 0.0001 51 | 0.13570135831832886 | 0.5899390243902439 | 0.8056984566679858 | 0.5639229458996943 | 1e-05 52 | 0.13534915447235107 | 0.5876524390243902 | 0.803262383131092 | 0.5523965140127979 | 1e-05 53 | 0.13460643589496613 | 0.5891768292682927 | 0.8047619047619048 | 0.5553165219596603 | 1e-05 54 | 0.13505160808563232 | 0.586890243902439 | 0.80398406374502 | 0.5513512052592927 | 1e-05 55 | 0.13664333522319794 | 0.5899390243902439 | 0.8044295036582955 | 0.5586243767362515 | 1e-05 56 | 0.13582760095596313 | 0.5876524390243902 | 0.8056215360253365 | 0.559616045636613 | 1e-05 57 | 0.1356770098209381 | 0.5891768292682927 | 0.8046205935072694 | 0.5592413950447728 | 1e-05 58 | 0.13557715713977814 | 0.586890243902439 | 0.804201347602061 | 0.5652648109787017 | 1e-05 59 | 0.13481777906417847 | 0.5884146341463414 | 0.806522171405846 | 0.564174198827338 | 1e-05 60 | 0.13674791157245636 | 0.5899390243902439 | 0.8047030689517736 | 0.5543815900756193 | 1.0000000000000002e-06 61 | 0.13405902683734894 | 0.5876524390243902 | 0.8045563549160671 | 0.5561036196553861 | 1.0000000000000002e-06 62 | 0.134480819106102 | 0.5861280487804879 | 0.8052360174533915 | 0.5646122700508593 | 1.0000000000000002e-06 63 | 0.13709656894207 | 0.5899390243902439 | 0.805958291956306 | 0.5660740924208384 | 1.0000000000000002e-06 64 | 0.13475064933300018 | 0.5929878048780488 | 0.8056551174830745 | 0.5521602551489271 | 1.0000000000000002e-06 65 | 0.13584908843040466 | 0.5899390243902439 | 0.8048345551812959 | 0.5594424812390786 | 1.0000000000000002e-06 66 | 0.13522611558437347 | 0.5853658536585366 | 0.8040500297796307 | 0.5680592850157375 | 1.0000000000000002e-06 67 | 0.1348220705986023 | 0.5876524390243902 | 0.8059288537549407 | 0.5689511664868826 | 1.0000000000000002e-06 68 | 0.13475003838539124 | 0.5853658536585366 | 0.8046843985708614 | 0.5680973012829017 | 1.0000000000000002e-07 69 | 0.13508079946041107 | 0.5884146341463414 | 0.8036036036036036 | 0.5523522321966495 | 1.0000000000000002e-07 70 | 0.13654960691928864 | 0.5853658536585366 | 0.8053744319304486 | 0.567977027563922 | 1.0000000000000002e-07 71 | 0.13474920392036438 | 0.5907012195121951 | 0.8072885719944544 | 0.5700988754371263 | 1.0000000000000002e-07 --- # CO2 Emissions The estimated CO2 emissions for training this model are documented below: - **Emissions**: 0.2101236289384968 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