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--- |
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language: |
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- eng |
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license: wtfpl |
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tags: |
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- multilabel-image-classification |
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- multilabel |
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- generated_from_trainer |
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base_model: facebook/dinov2-large |
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model-index: |
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- name: dinov2-large-2024_05_23-drone_batch-size512_epochs50_freeze |
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results: [] |
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--- |
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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: |
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- Loss: 0.2361 |
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- F1 Micro: 0.7694 |
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- F1 Macro: 0.4048 |
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- Roc Auc: 0.8448 |
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- Accuracy: 0.1449 |
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--- |
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# Model description |
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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. |
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The source code for training the model can be found in this [Git repository](https://github.com/SeatizenDOI/DinoVdeau). |
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- **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg) |
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--- |
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# Intended uses & limitations |
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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. |
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--- |
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# Training and evaluation data |
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Details on the number of images for each class are given in the following table: |
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| Class | train | val | test | Total | |
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|:-------------------------|--------:|------:|-------:|--------:| |
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| Acropore_branched | 1575 | 562 | 565 | 2702 | |
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| Acropore_digitised | 1020 | 356 | 370 | 1746 | |
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| Acropore_sub_massive | 198 | 56 | 60 | 314 | |
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| Acropore_tabular | 659 | 248 | 238 | 1145 | |
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| Algae_assembly | 7175 | 2447 | 2430 | 12052 | |
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| Algae_drawn_up | 439 | 156 | 156 | 751 | |
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| Algae_limestone | 4694 | 1576 | 1523 | 7793 | |
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| Algae_sodding | 7151 | 2460 | 2467 | 12078 | |
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| Bleached_coral | 352 | 162 | 150 | 664 | |
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| Dead_coral | 4615 | 1589 | 1553 | 7757 | |
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| Living_coral | 85 | 37 | 28 | 150 | |
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| Millepore | 860 | 287 | 313 | 1460 | |
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| No_acropore_encrusting | 1978 | 675 | 667 | 3320 | |
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| No_acropore_massive | 4539 | 1613 | 1585 | 7737 | |
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| No_acropore_sub_massive | 3696 | 1245 | 1252 | 6193 | |
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| Rock | 10810 | 3735 | 3718 | 18263 | |
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| Rubble | 9948 | 3429 | 3403 | 16780 | |
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| Sand | 10976 | 3659 | 3659 | 18294 | |
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| Sea_urchins | 400 | 147 | 135 | 682 | |
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| Sponge | 207 | 59 | 56 | 322 | |
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| Thalassodendron_ciliatum | 216 | 74 | 70 | 360 | |
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| Useless | 89 | 30 | 30 | 149 | |
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--- |
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# Training procedure |
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## Training hyperparameters |
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The following hyperparameters were used during training: |
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- **Number of Epochs**: 50 |
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- **Learning Rate**: 0.001 |
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- **Train Batch Size**: 512 |
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- **Eval Batch Size**: 512 |
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- **Optimizer**: Adam |
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- **LR Scheduler Type**: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1 |
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- **Freeze Encoder**: Yes |
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- **Data Augmentation**: Yes |
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## Data Augmentation |
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Data were augmented using the following transformations : |
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Train Transforms |
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- **PreProcess**: No additional parameters |
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- **Resize**: probability=1.00 |
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- **RandomHorizontalFlip**: probability=0.25 |
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- **RandomVerticalFlip**: probability=0.25 |
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- **ColorJiggle**: probability=0.25 |
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- **RandomPerspective**: probability=0.25 |
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- **Normalize**: probability=1.00 |
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Val Transforms |
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- **PreProcess**: No additional parameters |
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- **Resize**: probability=1.00 |
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- **Normalize**: probability=1.00 |
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## Training results |
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Epoch | Validation Loss | Accuracy | F1 Macro | F1 Micro | Learning Rate |
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--- | --- | --- | --- | --- | --- |
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1.0 | 0.5951732397079468 | 0.012405938580435224 | 0.5738973203699311 | 0.40667627783698285 | 0.001 |
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2.0 | 0.4730209410190582 | 0.06975798250966037 | 0.7307120964254151 | 0.4367882492755507 | 0.001 |
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3.0 | 0.3240152895450592 | 0.10738255033557047 | 0.7498981835953409 | 0.37702459211637257 | 0.001 |
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4.0 | 0.2770342230796814 | 0.11795810453528574 | 0.7521195160095482 | 0.3710481900670742 | 0.001 |
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5.0 | 0.25879302620887756 | 0.11958511287370348 | 0.7507292550220328 | 0.3714736793659693 | 0.001 |
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6.0 | 0.25328728556632996 | 0.12182224933902787 | 0.7520252586099456 | 0.36304822534346387 | 0.001 |
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7.0 | 0.25132349133491516 | 0.11531421598535692 | 0.7517183920016662 | 0.3646331511607325 | 0.001 |
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8.0 | 0.2507544159889221 | 0.12283912955053895 | 0.7576399892988702 | 0.38940077262215617 | 0.001 |
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9.0 | 0.24785615503787994 | 0.12751677852348994 | 0.7549859932265752 | 0.38290945223752887 | 0.001 |
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10.0 | 0.2480766475200653 | 0.12649989831197886 | 0.7583163191651716 | 0.37973264961121395 | 0.001 |
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11.0 | 0.24667006731033325 | 0.12426276184665447 | 0.7600958878849345 | 0.3964288145693209 | 0.001 |
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12.0 | 0.2459569126367569 | 0.12507626601586333 | 0.7564640698455339 | 0.3733203958034908 | 0.001 |
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13.0 | 0.245611771941185 | 0.1297539149888143 | 0.7581923944769908 | 0.38618999344086086 | 0.001 |
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14.0 | 0.24649737775325775 | 0.13707545251169412 | 0.7526021832952525 | 0.37084554766098704 | 0.001 |
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15.0 | 0.24523988366127014 | 0.1271100264388855 | 0.7540528606572888 | 0.37953234953900117 | 0.001 |
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16.0 | 0.24370642006397247 | 0.1293471629042099 | 0.7597242635642867 | 0.39042586476441543 | 0.001 |
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17.0 | 0.24466517567634583 | 0.13158429936953428 | 0.7525727259224682 | 0.38542350135117487 | 0.001 |
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17.857142857142858 | N/A | N/A | N/A | N/A | 0.001 |
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18.0 | 0.24544650316238403 | 0.133211307707952 | 0.7534316217590239 | 0.35783734462173733 | 0.001 |
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19.0 | 0.2440878450870514 | 0.13239780353874314 | 0.7568417082268136 | 0.3694145346248099 | 0.001 |
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20.0 | 0.2453632354736328 | 0.13605857230018303 | 0.750895096799091 | 0.3768127127776539 | 0.001 |
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21.0 | 0.24377579987049103 | 0.12487288997356112 | 0.760243826841616 | 0.38961590782494593 | 0.001 |
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22.0 | 0.24192409217357635 | 0.13016066707341875 | 0.7576183975637929 | 0.3715634230883189 | 0.001 |
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23.0 | 0.24348826706409454 | 0.12649989831197886 | 0.7628996647313762 | 0.3880375815747224 | 0.001 |
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24.0 | 0.2413305789232254 | 0.1342281879194631 | 0.7561114991428027 | 0.3896884130115941 | 0.001 |
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25.0 | 0.24189460277557373 | 0.1297539149888143 | 0.7599182173024102 | 0.38267978517684004 | 0.001 |
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26.0 | 0.2437727451324463 | 0.12670327435428105 | 0.7593076827294236 | 0.3971421437602147 | 0.001 |
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27.0 | 0.24182096123695374 | 0.1309741712426276 | 0.761437908496732 | 0.38383597863653807 | 0.001 |
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28.0 | 0.24316559731960297 | 0.13341468375025423 | 0.7498440155769273 | 0.3792682503180625 | 0.001 |
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29.0 | 0.24201267957687378 | 0.1366687004270897 | 0.7621594930458399 | 0.39596794972011545 | 0.001 |
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30.0 | 0.2406790852546692 | 0.14236322961155176 | 0.7619565217391304 | 0.38596411111358153 | 0.001 |
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31.0 | 0.24222084879875183 | 0.13280455562334756 | 0.7611869607298037 | 0.3928781445591724 | 0.001 |
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32.0 | 0.24304261803627014 | 0.13117754728492984 | 0.7516135926480015 | 0.3912203123758987 | 0.001 |
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33.0 | 0.24139608442783356 | 0.13016066707341875 | 0.758885526453094 | 0.38844227395152936 | 0.001 |
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34.0 | 0.24039919674396515 | 0.1354484441732764 | 0.7624706542289075 | 0.4037409737349212 | 0.001 |
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35.0 | 0.24134761095046997 | 0.12995729103111653 | 0.7601615858737297 | 0.3973020120442106 | 0.001 |
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35.714285714285715 | N/A | N/A | N/A | N/A | 0.001 |
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36.0 | 0.24192169308662415 | 0.13565182021557862 | 0.7622066694112803 | 0.38761085480429286 | 0.001 |
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37.0 | 0.2399486005306244 | 0.1342281879194631 | 0.7598352387357096 | 0.3992187594370792 | 0.001 |
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38.0 | 0.24004822969436646 | 0.13300793166564978 | 0.7607364527387098 | 0.3932700433432016 | 0.001 |
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39.0 | 0.24091550707817078 | 0.13890583689241406 | 0.7619087275149901 | 0.4007929579258356 | 0.001 |
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40.0 | 0.23991511762142181 | 0.1354484441732764 | 0.76 | 0.39250375468507387 | 0.001 |
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41.0 | 0.2422637641429901 | 0.12487288997356112 | 0.7639710985018574 | 0.40608061408264917 | 0.001 |
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42.0 | 0.24256455898284912 | 0.1309741712426276 | 0.7568840806286871 | 0.4005098857996497 | 0.001 |
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43.0 | 0.23922023177146912 | 0.13361805979255645 | 0.7594289817122102 | 0.4007981173529554 | 0.001 |
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44.0 | 0.24184103310108185 | 0.13036404311572097 | 0.7576905272903253 | 0.40641694858015515 | 0.001 |
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45.0 | 0.24105145037174225 | 0.13788895668090298 | 0.7591085068536151 | 0.39055068831340933 | 0.001 |
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46.0 | 0.23963303864002228 | 0.13626194834248526 | 0.7653508320819534 | 0.4106196361694743 | 0.001 |
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47.0 | 0.23957742750644684 | 0.13260117958104536 | 0.7575076348829317 | 0.3967990889217657 | 0.001 |
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48.0 | 0.24231907725334167 | 0.12873703477730322 | 0.7563947423325684 | 0.38777990454974365 | 0.001 |
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49.0 | 0.23978127539157867 | 0.13300793166564978 | 0.7608376348147216 | 0.40266317126303974 | 0.001 |
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50.0 | 0.23673731088638306 | 0.14236322961155176 | 0.7652267908369019 | 0.4087415721658059 | 0.0001 |
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--- |
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# CO2 Emissions |
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The estimated CO2 emissions for training this model are documented below: |
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- **Emissions**: 0.02562211166966913 grams of CO2 |
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- **Source**: Code Carbon |
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- **Training Type**: fine-tuning |
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- **Geographical Location**: Brest, France |
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- **Hardware Used**: NVIDIA Tesla V100 PCIe 32 Go |
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# Framework Versions |
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- **Transformers**: 4.41.0 |
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- **Pytorch**: 2.3.0+cu118 |
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- **Datasets**: 2.19.1 |
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- **Tokenizers**: 0.19.1 |
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