--- language: - eng license: wtfpl tags: - multilabel-image-classification - multilabel - generated_from_trainer base_model: microsoft/resnet-50 model-index: - name: resnet-50-2024_09_13-batch-size32_epochs150_freeze results: [] --- DinoVd'eau is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50). It achieves the following results on the test set: - Loss: nan - F1 Micro: 0.0002 - F1 Macro: 0.0002 - Roc Auc: 0.4995 - Accuracy: 0.0003 --- # 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 | |:-------------------------|--------:|------:|-------:|--------:| | Acropore_branched | 1469 | 464 | 475 | 2408 | | Acropore_digitised | 568 | 160 | 160 | 888 | | Acropore_sub_massive | 150 | 50 | 43 | 243 | | Acropore_tabular | 999 | 297 | 293 | 1589 | | Algae_assembly | 2546 | 847 | 845 | 4238 | | Algae_drawn_up | 367 | 126 | 127 | 620 | | Algae_limestone | 1652 | 557 | 563 | 2772 | | Algae_sodding | 3148 | 984 | 985 | 5117 | | Atra/Leucospilota | 1084 | 348 | 360 | 1792 | | Bleached_coral | 219 | 71 | 70 | 360 | | Blurred | 191 | 67 | 62 | 320 | | Dead_coral | 1979 | 642 | 643 | 3264 | | Fish | 2018 | 656 | 647 | 3321 | | Homo_sapiens | 161 | 62 | 59 | 282 | | Human_object | 157 | 58 | 55 | 270 | | Living_coral | 406 | 154 | 141 | 701 | | Millepore | 385 | 127 | 125 | 637 | | No_acropore_encrusting | 441 | 130 | 154 | 725 | | No_acropore_foliaceous | 204 | 36 | 46 | 286 | | No_acropore_massive | 1031 | 336 | 338 | 1705 | | No_acropore_solitary | 202 | 53 | 48 | 303 | | No_acropore_sub_massive | 1401 | 433 | 422 | 2256 | | Rock | 4489 | 1495 | 1473 | 7457 | | Rubble | 3092 | 1030 | 1001 | 5123 | | Sand | 5842 | 1939 | 1938 | 9719 | | Sea_cucumber | 1408 | 439 | 447 | 2294 | | Sea_urchins | 327 | 107 | 111 | 545 | | Sponge | 269 | 96 | 105 | 470 | | Syringodium_isoetifolium | 1212 | 392 | 391 | 1995 | | Thalassodendron_ciliatum | 782 | 261 | 260 | 1303 | | Useless | 579 | 193 | 193 | 965 | --- # 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 | nan | 0.0 | 0.0 | 0.0 | 0.001 2 | nan | 0.000693000693000693 | 0.00031409501374165687 | 0.00040576181781294376 | 0.001 3 | nan | 0.0017325017325017325 | 0.0007850525985241011 | 0.0010049241282283187 | 0.001 4 | nan | 0.0 | 0.0 | 0.0 | 0.001 5 | nan | 0.0010395010395010396 | 0.00047177229124076113 | 0.0006430178973314757 | 0.001 6 | nan | 0.0003465003465003465 | 0.00015712153350616704 | 0.000206782464846981 | 0.001 7 | nan | 0.0 | 0.0 | 0.0 | 0.0001 8 | nan | 0.0003465003465003465 | 0.00015710919088766695 | 0.0002061218179944347 | 0.0001 9 | nan | 0.0 | 0.0 | 0.0 | 0.0001 10 | nan | 0.000693000693000693 | 0.00031441597233139445 | 0.0004230565838180856 | 0.0001 11 | nan | 0.0 | 0.0 | 0.0 | 0.0001 --- # CO2 Emissions The estimated CO2 emissions for training this model are documented below: - **Emissions**: 0.12280230273705112 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