--- language: - eng license: cc0-1.0 tags: - multilabel-image-classification - multilabel - generated_from_trainer base_model: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs model-index: - name: bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs results: [] --- bd_ortho-DinoVdeau is a fine-tuned version of [bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs](https://huggingface.co/bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs). It achieves the following results on the test set: - Loss: 0.4551 - RMSE: 0.0866 - MAE: 0.0630 - KL Divergence: 0.1147 --- # Model description bd_ortho-DinoVdeau is a model built on top of bd_ortho-DinoVdeau-large-2024_11_27-batch-size64_freeze_probs 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 estimated number of images for each class are given in the following table: | Class | train | test | val | Total | |:------------------------|--------:|-------:|------:|--------:| | Acropore_branched | 8074 | 2667 | 2658 | 13399 | | Acropore_digitised | 3730 | 829 | 823 | 5382 | | Acropore_tabular | 125 | 23 | 40 | 188 | | Algae | 14027 | 4662 | 4660 | 23349 | | Dead_coral | 11369 | 3364 | 3369 | 18102 | | Millepore | 2 | 1 | 1 | 4 | | No_acropore_encrusting | 0 | 0 | 0 | 0 | | No_acropore_massive | 3265 | 423 | 463 | 4151 | | No_acropore_sub_massive | 10241 | 2911 | 2924 | 16076 | | Rock | 14090 | 4694 | 4693 | 23477 | | Rubble | 12455 | 3915 | 3883 | 20253 | | Sand | 12848 | 4098 | 4079 | 21025 | --- # Training procedure ## Training hyperparameters The following hyperparameters were used during training: - **Number of Epochs**: 62.0 - **Learning Rate**: 0.001 - **Train Batch Size**: 64 - **Eval Batch Size**: 64 - **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 | MAE | RMSE | KL div | Learning Rate --- | --- | --- | --- | --- | --- 1 | 0.46336060762405396 | 0.0760 | 0.1018 | 0.0696 | 0.001 2 | 0.45933997631073 | 0.0716 | 0.0952 | 0.0038 | 0.001 3 | 0.457367479801178 | 0.0670 | 0.0918 | 0.0583 | 0.001 4 | 0.459468811750412 | 0.0713 | 0.0955 | -0.0650 | 0.001 5 | 0.45927393436431885 | 0.0702 | 0.0954 | -0.0835 | 0.001 6 | 0.46080395579338074 | 0.0728 | 0.0977 | -0.0705 | 0.001 7 | 0.4581476151943207 | 0.0683 | 0.0927 | -0.0044 | 0.001 8 | 0.4573117196559906 | 0.0680 | 0.0916 | 0.0799 | 0.001 9 | 0.45939013361930847 | 0.0706 | 0.0947 | 0.0233 | 0.001 10 | 0.45772281289100647 | 0.0675 | 0.0918 | 0.0885 | 0.001 11 | 0.45641985535621643 | 0.0662 | 0.0898 | 0.1296 | 0.001 12 | 0.45718902349472046 | 0.0677 | 0.0913 | -0.0061 | 0.001 13 | 0.4622880220413208 | 0.0747 | 0.1002 | -0.2060 | 0.001 14 | 0.45775285363197327 | 0.0678 | 0.0925 | -0.0371 | 0.001 15 | 0.4575214684009552 | 0.0667 | 0.0917 | 0.0458 | 0.001 16 | 0.4578736424446106 | 0.0680 | 0.0926 | 0.0151 | 0.001 17 | 0.4592094421386719 | 0.0702 | 0.0949 | -0.0679 | 0.001 18 | 0.45573291182518005 | 0.0651 | 0.0887 | 0.0421 | 0.0001 19 | 0.4555513262748718 | 0.0647 | 0.0885 | 0.0468 | 0.0001 20 | 0.45553284883499146 | 0.0648 | 0.0884 | 0.0405 | 0.0001 21 | 0.4555487334728241 | 0.0650 | 0.0884 | 0.0475 | 0.0001 22 | 0.45551028847694397 | 0.0646 | 0.0883 | 0.0570 | 0.0001 23 | 0.45505577325820923 | 0.0641 | 0.0874 | 0.0887 | 0.0001 24 | 0.4552234709262848 | 0.0642 | 0.0878 | 0.0555 | 0.0001 25 | 0.45521080493927 | 0.0645 | 0.0878 | 0.0238 | 0.0001 26 | 0.4557025730609894 | 0.0646 | 0.0885 | 0.0409 | 0.0001 27 | 0.4550967216491699 | 0.0639 | 0.0876 | 0.0548 | 0.0001 28 | 0.45512688159942627 | 0.0642 | 0.0876 | 0.0273 | 0.0001 29 | 0.45477041602134705 | 0.0634 | 0.0869 | 0.0744 | 0.0001 30 | 0.4549327790737152 | 0.0636 | 0.0873 | 0.0492 | 0.0001 31 | 0.4547973871231079 | 0.0632 | 0.0869 | 0.0688 | 0.0001 32 | 0.454988956451416 | 0.0639 | 0.0874 | 0.0271 | 0.0001 33 | 0.455375999212265 | 0.0647 | 0.0882 | -0.0174 | 0.0001 34 | 0.45461305975914 | 0.0628 | 0.0866 | 0.1094 | 0.0001 35 | 0.45498156547546387 | 0.0639 | 0.0874 | 0.0571 | 0.0001 36 | 0.4548388123512268 | 0.0629 | 0.0869 | 0.1453 | 0.0001 37 | 0.45526784658432007 | 0.0645 | 0.0881 | -0.0152 | 0.0001 38 | 0.45479556918144226 | 0.0636 | 0.0870 | 0.0490 | 0.0001 39 | 0.454780250787735 | 0.0631 | 0.0870 | 0.0726 | 0.0001 40 | 0.45476558804512024 | 0.0632 | 0.0870 | 0.0637 | 0.0001 41 | 0.45470812916755676 | 0.0634 | 0.0869 | 0.0390 | 1e-05 42 | 0.4543863534927368 | 0.0628 | 0.0862 | 0.1115 | 1e-05 43 | 0.4545557498931885 | 0.0632 | 0.0866 | 0.0533 | 1e-05 44 | 0.45448434352874756 | 0.0625 | 0.0864 | 0.1350 | 1e-05 45 | 0.4550137519836426 | 0.0642 | 0.0874 | 0.0044 | 1e-05 46 | 0.4545902609825134 | 0.0632 | 0.0867 | 0.0389 | 1e-05 47 | 0.4544997215270996 | 0.0630 | 0.0866 | 0.0370 | 1e-05 48 | 0.4546374976634979 | 0.0634 | 0.0868 | 0.0194 | 1e-05 49 | 0.45436596870422363 | 0.0627 | 0.0862 | 0.0667 | 1.0000000000000002e-06 50 | 0.45450592041015625 | 0.0631 | 0.0865 | 0.0548 | 1.0000000000000002e-06 51 | 0.4544804096221924 | 0.0629 | 0.0865 | 0.0428 | 1.0000000000000002e-06 52 | 0.45421910285949707 | 0.0623 | 0.0859 | 0.1236 | 1.0000000000000002e-06 53 | 0.4542272686958313 | 0.0625 | 0.0859 | 0.0887 | 1.0000000000000002e-06 54 | 0.4543103575706482 | 0.0624 | 0.0862 | 0.0917 | 1.0000000000000002e-06 55 | 0.45456644892692566 | 0.0631 | 0.0865 | 0.0774 | 1.0000000000000002e-06 56 | 0.45458319783210754 | 0.0633 | 0.0866 | 0.0473 | 1.0000000000000002e-06 57 | 0.4548773169517517 | 0.0639 | 0.0871 | -0.0046 | 1.0000000000000002e-06 58 | 0.45440155267715454 | 0.0627 | 0.0864 | 0.0553 | 1.0000000000000002e-06 59 | 0.45448538661003113 | 0.0631 | 0.0865 | 0.0368 | 1.0000000000000002e-07 60 | 0.4544091522693634 | 0.0629 | 0.0863 | 0.0471 | 1.0000000000000002e-07 61 | 0.4542348086833954 | 0.0624 | 0.0860 | 0.0928 | 1.0000000000000002e-07 62 | 0.4545469284057617 | 0.0632 | 0.0866 | 0.0286 | 1.0000000000000002e-07 --- # Framework Versions - **Transformers**: 4.41.0 - **Pytorch**: 2.5.0+cu124 - **Datasets**: 3.0.2 - **Tokenizers**: 0.19.1