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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: Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs |
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model-index: |
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- name: Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs |
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results: [] |
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--- |
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Ziboiai is a fine-tuned version of [Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs](https://huggingface.co/Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs). It achieves the following results on the test set: |
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- Loss: 0.6188 |
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- F1 Micro: 0.9261 |
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- F1 Macro: 0.8546 |
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- Accuracy: 0.1600 |
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- RMSE: 0.3415 |
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- MAE: 0.3061 |
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- R2: -1.5955 |
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| Class | F1 per class | |
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|----------|-------| |
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| Acropore_branched | 0.8966 | |
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| Acropore_digitised | 0.6301 | |
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| Acropore_tabular | 1.0000 | |
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| Algae | 1.0000 | |
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| Dead_coral | 0.8395 | |
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| Fish | 0.8861 | |
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| Millepore | 1.0000 | |
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| No_acropore_encrusting | 1.0000 | |
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| No_acropore_massive | 0.0000 | |
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| No_acropore_sub_massive | 0.8571 | |
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| Rock | 1.0000 | |
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| Rubble | 1.0000 | |
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| Sand | 1.0000 | |
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--- |
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# Model description |
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Ziboiai is a model built on top of Ziboiai-large-2024_10_31-prova_batch-size32_freeze_probs 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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# Training and evaluation data |
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Details on the estimated 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 | 41 | 32 | 33 | 106 | |
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| Acropore_digitised | 15 | 14 | 14 | 43 | |
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| Acropore_tabular | 5 | 8 | 7 | 20 | |
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| Algae | 50 | 50 | 50 | 150 | |
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| Dead_coral | 25 | 28 | 30 | 83 | |
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| Fish | 34 | 24 | 31 | 89 | |
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| Millepore | 1 | 0 | 0 | 1 | |
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| No_acropore_encrusting | 1 | 0 | 0 | 1 | |
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| No_acropore_massive | 2 | 5 | 5 | 12 | |
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| No_acropore_sub_massive | 27 | 28 | 27 | 82 | |
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| Rock | 45 | 47 | 45 | 137 | |
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| Rubble | 40 | 45 | 44 | 129 | |
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| Sand | 42 | 46 | 45 | 133 | |
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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**: 40.0 |
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- **Learning Rate**: 0.001 |
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- **Train Batch Size**: 32 |
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- **Eval Batch Size**: 32 |
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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 | MAE | RMSE | R2 | Learning Rate |
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--- | --- | --- | --- | --- | --- |
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1 | 0.7150455713272095 | 0.3848940134048462 | 0.40997111797332764 | -20.29086685180664 | 0.001 |
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2 | 0.7314126491546631 | 0.3895121216773987 | 0.4163060486316681 | -21.218204498291016 | 0.001 |
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3 | 0.7726277112960815 | 0.40413352847099304 | 0.4320966601371765 | -24.822391510009766 | 0.001 |
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4 | 0.7917326092720032 | 0.4094983637332916 | 0.4379725754261017 | -26.581586837768555 | 0.001 |
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5 | 0.7852649092674255 | 0.402120441198349 | 0.43184274435043335 | -26.95589256286621 | 0.001 |
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6 | 0.7647674679756165 | 0.3905399441719055 | 0.42244094610214233 | -24.40153694152832 | 0.001 |
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7 | 0.7391812205314636 | 0.376028835773468 | 0.41028541326522827 | -22.557889938354492 | 0.001 |
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8 | 0.7115270495414734 | 0.36385056376457214 | 0.39825379848480225 | -20.067392349243164 | 0.0001 |
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9 | 0.6896975040435791 | 0.35347798466682434 | 0.3878582715988159 | -18.16646385192871 | 0.0001 |
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10 | 0.6777035593986511 | 0.34683120250701904 | 0.3818005323410034 | -16.94469451904297 | 0.0001 |
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11 | 0.6701759099960327 | 0.3423532247543335 | 0.3779585659503937 | -16.037521362304688 | 0.0001 |
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12 | 0.663905918598175 | 0.3388546407222748 | 0.37438222765922546 | -15.605177879333496 | 0.0001 |
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13 | 0.656491219997406 | 0.3345881700515747 | 0.3702985942363739 | -14.805088996887207 | 0.0001 |
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14 | 0.6501385569572449 | 0.33100754022598267 | 0.3668138384819031 | -14.231175422668457 | 0.0001 |
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15 | 0.6467865705490112 | 0.32885220646858215 | 0.36475783586502075 | -14.07986831665039 | 0.0001 |
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16 | 0.6471170783042908 | 0.3288896679878235 | 0.3650059998035431 | -14.255745887756348 | 0.0001 |
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17 | 0.6435126662254333 | 0.3268200755119324 | 0.36310678720474243 | -14.059813499450684 | 0.0001 |
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18 | 0.6437923908233643 | 0.3269612491130829 | 0.36342939734458923 | -14.036934852600098 | 0.0001 |
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19 | 0.6399621367454529 | 0.3249860107898712 | 0.36136963963508606 | -13.81522274017334 | 0.0001 |
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20 | 0.6391971707344055 | 0.3246455192565918 | 0.3608955144882202 | -13.710391998291016 | 0.0001 |
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21 | 0.6386714577674866 | 0.32462170720100403 | 0.3606450855731964 | -13.809860229492188 | 0.0001 |
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22 | 0.6388444304466248 | 0.3243348002433777 | 0.36056435108184814 | -13.849721908569336 | 0.0001 |
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23 | 0.6361631155014038 | 0.3227779269218445 | 0.35895633697509766 | -13.562189102172852 | 0.0001 |
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24 | 0.635435163974762 | 0.3223152160644531 | 0.35847193002700806 | -13.645319938659668 | 0.0001 |
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25 | 0.6344550848007202 | 0.32144099473953247 | 0.35783687233924866 | -13.602314949035645 | 0.0001 |
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26 | 0.6348865628242493 | 0.3211889863014221 | 0.3580625355243683 | -13.630416870117188 | 0.0001 |
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27 | 0.6332749724388123 | 0.32009246945381165 | 0.3570806384086609 | -13.561347007751465 | 0.0001 |
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28 | 0.6295092701911926 | 0.31767499446868896 | 0.35479238629341125 | -13.23308277130127 | 0.0001 |
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29 | 0.6285346746444702 | 0.3173280954360962 | 0.35434553027153015 | -13.162256240844727 | 0.0001 |
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30 | 0.6263097524642944 | 0.31627562642097473 | 0.3532228171825409 | -12.713174819946289 | 0.0001 |
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31 | 0.6272528767585754 | 0.316723495721817 | 0.35376670956611633 | -12.873921394348145 | 0.0001 |
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32 | 0.6294133067131042 | 0.31807586550712585 | 0.3550169765949249 | -12.935453414916992 | 0.0001 |
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33 | 0.6299176216125488 | 0.3185364603996277 | 0.35538923740386963 | -12.93520736694336 | 0.0001 |
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34 | 0.6320692300796509 | 0.3193182349205017 | 0.35644862055778503 | -13.267191886901855 | 0.0001 |
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35 | 0.6279481649398804 | 0.31752488017082214 | 0.3541102707386017 | -12.99951171875 | 0.0001 |
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36 | 0.6280075907707214 | 0.31736499071121216 | 0.35407301783561707 | -13.00741195678711 | 0.0001 |
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37 | 0.6303659081459045 | 0.3187006115913391 | 0.35543760657310486 | -13.230977058410645 | 1e-05 |
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38 | 0.6297122836112976 | 0.31833118200302124 | 0.3550592064857483 | -12.983016967773438 | 1e-05 |
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39 | 0.630845308303833 | 0.3193325996398926 | 0.35580796003341675 | -13.159842491149902 | 1e-05 |
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40 | 0.6291573643684387 | 0.3182610869407654 | 0.3547934889793396 | -13.069788932800293 | 1e-05 |
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--- |
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# Framework Versions |
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- **Transformers**: 4.44.2 |
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- **Pytorch**: 2.4.1+cu121 |
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- **Datasets**: 3.0.0 |
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- **Tokenizers**: 0.19.1 |
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