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--- |
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language: |
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- eng |
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license: cc0-1.0 |
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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: drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze |
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model-index: |
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- name: drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze |
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results: [] |
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--- |
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drone-DinoVdeau-produttoria_binary-binary is a fine-tuned version of [drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze](https://huggingface.co/drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze). It achieves the following results on the test set: |
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- Loss: 0.2854 |
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- F1 Micro: 0.8468 |
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- F1 Macro: 0.6351 |
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- Accuracy: 0.2786 |
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| Class | F1 per class | |
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|----------|-------| |
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| Acropore_branched | 0.8084 | |
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| Acropore_digitised | 0.5125 | |
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| Acropore_tabular | 0.3951 | |
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| Algae | 0.9562 | |
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| Dead_coral | 0.7470 | |
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| Fish | 0.6639 | |
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| Millepore | 0.3021 | |
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| No_acropore_encrusting | 0.5923 | |
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| No_acropore_massive | 0.7651 | |
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| No_acropore_sub_massive | 0.6345 | |
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| Rock | 0.9536 | |
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| Rubble | 0.9042 | |
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| Sand | 0.9008 | |
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--- |
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# Model description |
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drone-DinoVdeau-produttoria_binary-binary is a model built on top of drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze 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 | test | val | Total | |
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|:------------------------|--------:|-------:|------:|--------:| |
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| Acropore_branched | 1483 | 522 | 529 | 2534 | |
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| Acropore_digitised | 1085 | 371 | 362 | 1818 | |
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| Acropore_tabular | 486 | 176 | 178 | 840 | |
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| Algae | 10340 | 3441 | 3461 | 17242 | |
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| Dead_coral | 3710 | 1252 | 1267 | 6229 | |
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| Fish | 1462 | 517 | 515 | 2494 | |
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| Millepore | 746 | 282 | 273 | 1301 | |
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| No_acropore_encrusting | 1993 | 751 | 728 | 3472 | |
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| No_acropore_massive | 4450 | 1581 | 1649 | 7680 | |
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| No_acropore_sub_massive | 3034 | 1102 | 1113 | 5249 | |
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| Rock | 10225 | 3429 | 3445 | 17099 | |
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| Rubble | 9353 | 3100 | 3105 | 15558 | |
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| Sand | 9271 | 3101 | 3132 | 15504 | |
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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**: 88.0 |
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- **Learning Rate**: 0.001 |
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- **Train Batch Size**: 64 |
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- **Eval Batch Size**: 64 |
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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 | 0.3235681354999542 | 0.2630072840790843 | 0.8262109753225342 | 0.5774239185038708 | 0.001 |
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2 | 0.3146470785140991 | 0.24115504682622269 | 0.8378565084377776 | 0.6199165901601139 | 0.001 |
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3 | 0.3090434670448303 | 0.2554630593132154 | 0.8398465111582348 | 0.6043570009634397 | 0.001 |
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4 | 0.30735355615615845 | 0.25624349635796045 | 0.8348980169243037 | 0.600278483167516 | 0.001 |
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5 | 0.30385810136795044 | 0.2515608740894901 | 0.8405948994360434 | 0.6247746971203368 | 0.001 |
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6 | 0.3059956729412079 | 0.2596253902185224 | 0.841987466427932 | 0.6225111439021958 | 0.001 |
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7 | 0.3013758361339569 | 0.28199791883454733 | 0.8387498056289846 | 0.5954695621655504 | 0.001 |
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8 | 0.30131709575653076 | 0.2702913631633715 | 0.8390550208451284 | 0.5974832028652961 | 0.001 |
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9 | 0.30098479986190796 | 0.28407908428720086 | 0.8406665130922214 | 0.5974259992816957 | 0.001 |
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10 | 0.30072343349456787 | 0.27107180020811655 | 0.8376187886791475 | 0.5937940362628795 | 0.001 |
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11 | 0.3035621643066406 | 0.277315296566077 | 0.8348592565387339 | 0.5761905737205768 | 0.001 |
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12 | 0.3012838363647461 | 0.26742976066597296 | 0.838466245156027 | 0.6114755503631268 | 0.001 |
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13 | 0.29778778553009033 | 0.2648283038501561 | 0.8421213122252433 | 0.6145726431106396 | 0.001 |
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14 | 0.29774588346481323 | 0.27341311134235174 | 0.8399742101869762 | 0.605884177295118 | 0.001 |
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15 | 0.29809942841529846 | 0.2666493236212279 | 0.8433503513117323 | 0.6074624445346274 | 0.001 |
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16 | 0.29744812846183777 | 0.27471383975026015 | 0.8394100355835181 | 0.5932952143692389 | 0.001 |
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17 | 0.2983638644218445 | 0.2663891779396462 | 0.8437578624264077 | 0.6146867059353278 | 0.001 |
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18 | 0.3023049235343933 | 0.2762747138397503 | 0.8356339535005088 | 0.5803903225868541 | 0.001 |
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19 | 0.2984697222709656 | 0.2739334027055151 | 0.8423529411764706 | 0.6158875389283108 | 0.001 |
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20 | 0.29680272936820984 | 0.28069719042663893 | 0.8411767731317183 | 0.5984147849283556 | 0.001 |
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21 | 0.30051520466804504 | 0.2702913631633715 | 0.8418969323285377 | 0.6060492619397649 | 0.001 |
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22 | 0.29818177223205566 | 0.27471383975026015 | 0.8374817746302854 | 0.580353532272699 | 0.001 |
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23 | 0.29393449425697327 | 0.27809573361082207 | 0.8436262061960386 | 0.615237110287355 | 0.001 |
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24 | 0.2948347330093384 | 0.27601456815816855 | 0.8453232862164007 | 0.6228721497006335 | 0.001 |
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25 | 0.29676035046577454 | 0.2736732570239334 | 0.8427456149244652 | 0.610255370235793 | 0.001 |
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26 | 0.2955995500087738 | 0.2754942767950052 | 0.8420542140997499 | 0.6045462014226007 | 0.001 |
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27 | 0.29585039615631104 | 0.27653485952133194 | 0.8437684356323902 | 0.6115221375683754 | 0.001 |
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28 | 0.295540988445282 | 0.26925078043704476 | 0.8446938104986479 | 0.6191186747828321 | 0.001 |
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29 | 0.3010655343532562 | 0.2663891779396462 | 0.8437664387164651 | 0.6215750043898619 | 0.001 |
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30 | 0.29214760661125183 | 0.2809573361082206 | 0.8437435686355217 | 0.6025311078598518 | 0.0001 |
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31 | 0.29040178656578064 | 0.28121748178980227 | 0.8439103638567266 | 0.6071651131848005 | 0.0001 |
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32 | 0.29034462571144104 | 0.2809573361082206 | 0.8437194965322373 | 0.6111569473926136 | 0.0001 |
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33 | 0.2888760268688202 | 0.28537981269510926 | 0.8461617038663874 | 0.6202495870793918 | 0.0001 |
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34 | 0.28964364528656006 | 0.2861602497398543 | 0.8446023671361742 | 0.6150504150317478 | 0.0001 |
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35 | 0.28874215483665466 | 0.2866805411030177 | 0.8449244728566273 | 0.611180048847438 | 0.0001 |
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36 | 0.2888963222503662 | 0.28355879292403746 | 0.8447173058645225 | 0.6119874534823754 | 0.0001 |
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37 | 0.288282573223114 | 0.2866805411030177 | 0.8475834540970686 | 0.6255767175486281 | 0.0001 |
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38 | 0.29050976037979126 | 0.28251821019771073 | 0.8452536426724028 | 0.6057239934398935 | 0.0001 |
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39 | 0.28778275847435 | 0.28537981269510926 | 0.8470600182796791 | 0.625366961909805 | 0.0001 |
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40 | 0.2885717749595642 | 0.2809573361082206 | 0.8468000302716884 | 0.622337777946806 | 0.0001 |
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41 | 0.28773826360702515 | 0.2843392299687825 | 0.847323400258903 | 0.6260539681026288 | 0.0001 |
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42 | 0.28776827454566956 | 0.28563995837669093 | 0.8476613005450627 | 0.6199392946357273 | 0.0001 |
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43 | 0.28717148303985596 | 0.28303850156087407 | 0.8479237095716232 | 0.6287571427217789 | 0.0001 |
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44 | 0.28678667545318604 | 0.28407908428720086 | 0.8463665693654939 | 0.6189979239207937 | 0.0001 |
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45 | 0.28698909282684326 | 0.28381893860561913 | 0.8462928555066304 | 0.6235508782461164 | 0.0001 |
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46 | 0.2868472635746002 | 0.28251821019771073 | 0.8459846547314578 | 0.6151318511304835 | 0.0001 |
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47 | 0.28715068101882935 | 0.2845993756503642 | 0.8462129359348595 | 0.6211457155619424 | 0.0001 |
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48 | 0.28661593794822693 | 0.28355879292403746 | 0.8466852933705867 | 0.6231150403485404 | 0.0001 |
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49 | 0.28633347153663635 | 0.28590010405827265 | 0.8460415439387342 | 0.616055362439494 | 0.0001 |
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50 | 0.28642749786376953 | 0.2845993756503642 | 0.8482882700250868 | 0.625458075101288 | 0.0001 |
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51 | 0.2890762686729431 | 0.28485952133194586 | 0.848592785832539 | 0.6278100779578839 | 0.0001 |
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52 | 0.2855978012084961 | 0.2851196670135276 | 0.8464228285561143 | 0.6255462096645672 | 0.0001 |
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53 | 0.2872205674648285 | 0.27887617065556713 | 0.8489991514001897 | 0.6457587856102145 | 0.0001 |
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54 | 0.2855803072452545 | 0.2903225806451613 | 0.8476844874709444 | 0.6243869856844756 | 0.0001 |
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55 | 0.28568968176841736 | 0.2845993756503642 | 0.8475136716266056 | 0.6339630509281279 | 0.0001 |
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56 | 0.28617897629737854 | 0.2866805411030177 | 0.8465597622829039 | 0.6241465491773776 | 0.0001 |
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57 | 0.2870914936065674 | 0.2861602497398543 | 0.845436853426201 | 0.6249269702519318 | 0.0001 |
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58 | 0.2857914865016937 | 0.28121748178980227 | 0.8491941382702348 | 0.6333866717026029 | 0.0001 |
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59 | 0.28617140650749207 | 0.2887617065556712 | 0.8468232576049287 | 0.6178461796051926 | 1e-05 |
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60 | 0.2846605181694031 | 0.28537981269510926 | 0.8485033598045205 | 0.6275748058546806 | 1e-05 |
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61 | 0.2848633825778961 | 0.28303850156087407 | 0.8479865171982329 | 0.6223888517425455 | 1e-05 |
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62 | 0.28548601269721985 | 0.2843392299687825 | 0.8469200122586577 | 0.6247632003821695 | 1e-05 |
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63 | 0.28493326902389526 | 0.2827783558792924 | 0.8488979777323336 | 0.6274806463168713 | 1e-05 |
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64 | 0.28459736704826355 | 0.28225806451612906 | 0.8475187206498287 | 0.6370787064578803 | 1e-05 |
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65 | 0.2860054671764374 | 0.2869406867845994 | 0.8467700785794469 | 0.6240984315849201 | 1e-05 |
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66 | 0.2847185730934143 | 0.28407908428720086 | 0.8481340441736481 | 0.6346693986906206 | 1e-05 |
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67 | 0.28529325127601624 | 0.28537981269510926 | 0.8487528745798691 | 0.6287121285420982 | 1e-05 |
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68 | 0.2852926254272461 | 0.2866805411030177 | 0.8480251642525557 | 0.6321379394582358 | 1e-05 |
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69 | 0.284834623336792 | 0.28355879292403746 | 0.847692190707931 | 0.6397237492354447 | 1e-05 |
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70 | 0.28527727723121643 | 0.28225806451612906 | 0.8492167101827677 | 0.6381143671040704 | 1e-05 |
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71 | 0.28507113456726074 | 0.2882414151925078 | 0.8475971370143149 | 0.6325489300082728 | 1.0000000000000002e-06 |
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72 | 0.28452861309051514 | 0.28485952133194586 | 0.8474255781269963 | 0.6236352127811986 | 1.0000000000000002e-06 |
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73 | 0.28448227047920227 | 0.28121748178980227 | 0.847641772858811 | 0.6333277250193455 | 1.0000000000000002e-06 |
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74 | 0.28447526693344116 | 0.2827783558792924 | 0.8465770953294945 | 0.6300187593616763 | 1.0000000000000002e-06 |
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75 | 0.2851284146308899 | 0.28199791883454733 | 0.8473772748126625 | 0.6235297745568456 | 1.0000000000000002e-06 |
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76 | 0.2859683036804199 | 0.2879812695109261 | 0.847320835674516 | 0.6186062513830065 | 1.0000000000000002e-06 |
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77 | 0.2858298718929291 | 0.28563995837669093 | 0.8459046737621472 | 0.6172786558676017 | 1.0000000000000002e-06 |
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78 | 0.28438833355903625 | 0.2843392299687825 | 0.8480547459130655 | 0.6325947858436887 | 1.0000000000000002e-06 |
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79 | 0.2870919704437256 | 0.2874609781477627 | 0.8472353346431579 | 0.617917490234713 | 1.0000000000000002e-06 |
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80 | 0.28482332825660706 | 0.28381893860561913 | 0.8477330616403465 | 0.6286567457369128 | 1.0000000000000002e-06 |
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81 | 0.2847617268562317 | 0.28537981269510926 | 0.8489678202792957 | 0.6304525529970205 | 1.0000000000000002e-06 |
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82 | 0.28511229157447815 | 0.28590010405827265 | 0.8480416961845967 | 0.6394217270135759 | 1.0000000000000002e-06 |
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83 | 0.284644216299057 | 0.28563995837669093 | 0.8488055562622434 | 0.6255055774993536 | 1.0000000000000002e-06 |
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84 | 0.2857225835323334 | 0.2832986472424558 | 0.848188643119867 | 0.6457553263622914 | 1.0000000000000002e-06 |
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85 | 0.28550758957862854 | 0.28121748178980227 | 0.848818698673405 | 0.6339586571635658 | 1.0000000000000002e-07 |
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86 | 0.284895658493042 | 0.28590010405827265 | 0.8479890588592848 | 0.6362631688004041 | 1.0000000000000002e-07 |
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87 | 0.2845035493373871 | 0.2851196670135276 | 0.8473590201582036 | 0.6327749126527296 | 1.0000000000000002e-07 |
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88 | 0.28541097044944763 | 0.28121748178980227 | 0.8477551536613127 | 0.6370893160624239 | 1.0000000000000002e-07 |
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--- |
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# Framework Versions |
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- **Transformers**: 4.41.0 |
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- **Pytorch**: 2.5.0+cu124 |
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- **Datasets**: 3.0.2 |
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- **Tokenizers**: 0.19.1 |
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