--- language: - eng license: wtfpl tags: - multilabel-image-classification - multilabel - generated_from_trainer base_model: facebook/dinov2-large model-index: - name: drone-DinoVdeau-large-2024_09_16-batch-size64_epochs100_freeze results: [] --- 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: - Explained variance: 0.3677 - Loss: 0.3353 - MAE: 0.1229 - MSE: 0.0346 - R2: 0.3673 - RMSE: 0.1861 --- # 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 | 1956 | 650 | 653 | 3259 | | Acropore_digitised | 1717 | 577 | 575 | 2869 | | Acropore_tabular | 1105 | 375 | 388 | 1868 | | Algae | 11085 | 3678 | 3680 | 18443 | | Dead_coral | 5888 | 1953 | 1958 | 9799 | | Fish | 3453 | 1157 | 1157 | 5767 | | Millepore | 1779 | 666 | 698 | 3143 | | No_acropore_encrusting | 2726 | 966 | 988 | 4680 | | No_acropore_massive | 6486 | 2188 | 2138 | 10812 | | No_acropore_sub_massive | 5026 | 1772 | 1769 | 8567 | | Rock | 11176 | 3725 | 3725 | 18626 | | Rubble | 10689 | 3563 | 3563 | 17815 | | Sand | 11168 | 3723 | 3723 | 18614 | | Sea_cucumber | 2751 | 1065 | 1129 | 4945 | | Sea_urchins | 651 | 274 | 269 | 1194 | --- # Training procedure ## Training hyperparameters The following hyperparameters were used during training: - **Number of Epochs**: 100 - **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 | Explained Variance | Validation Loss | MAE | MSE | R2 | RMSE | Learning Rate --- | --- | --- | --- | --- | --- | --- | --- 1 | 0.254 | 0.361 | 0.147 | 0.043 | 0.244 | 0.207 | 0.001 2 | 0.297 | 0.35 | 0.134 | 0.04 | 0.294 | 0.201 | 0.001 3 | 0.301 | 0.349 | 0.134 | 0.04 | 0.299 | 0.2 | 0.001 4 | 0.303 | 0.349 | 0.132 | 0.04 | 0.298 | 0.2 | 0.001 5 | 0.309 | 0.348 | 0.138 | 0.04 | 0.304 | 0.199 | 0.001 6 | 0.319 | 0.346 | 0.136 | 0.039 | 0.316 | 0.197 | 0.001 7 | 0.316 | 0.345 | 0.133 | 0.039 | 0.313 | 0.197 | 0.001 8 | 0.322 | 0.344 | 0.132 | 0.038 | 0.32 | 0.196 | 0.001 9 | 0.328 | 0.343 | 0.131 | 0.038 | 0.327 | 0.195 | 0.001 10 | 0.331 | 0.343 | 0.134 | 0.038 | 0.327 | 0.195 | 0.001 11 | 0.327 | 0.344 | 0.135 | 0.039 | 0.322 | 0.196 | 0.001 12 | 0.335 | 0.342 | 0.129 | 0.038 | 0.332 | 0.195 | 0.001 13 | 0.333 | 0.342 | 0.132 | 0.038 | 0.33 | 0.195 | 0.001 14 | 0.327 | 0.343 | 0.131 | 0.038 | 0.325 | 0.196 | 0.001 15 | 0.333 | 0.344 | 0.135 | 0.038 | 0.328 | 0.196 | 0.001 16 | 0.331 | 0.342 | 0.131 | 0.038 | 0.329 | 0.195 | 0.001 17 | 0.332 | 0.342 | 0.131 | 0.038 | 0.331 | 0.195 | 0.001 18 | 0.326 | 0.346 | 0.135 | 0.039 | 0.319 | 0.198 | 0.001 19 | 0.343 | 0.34 | 0.13 | 0.037 | 0.343 | 0.193 | 0.0001 20 | 0.344 | 0.34 | 0.128 | 0.037 | 0.343 | 0.193 | 0.0001 21 | 0.348 | 0.339 | 0.129 | 0.037 | 0.348 | 0.192 | 0.0001 22 | 0.349 | 0.338 | 0.128 | 0.037 | 0.348 | 0.192 | 0.0001 23 | 0.349 | 0.338 | 0.129 | 0.037 | 0.348 | 0.192 | 0.0001 24 | 0.351 | 0.338 | 0.128 | 0.037 | 0.35 | 0.191 | 0.0001 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0001 26 | 0.354 | 0.337 | 0.128 | 0.036 | 0.353 | 0.191 | 0.0001 27 | 0.356 | 0.337 | 0.127 | 0.036 | 0.355 | 0.19 | 0.0001 28 | 0.356 | 0.337 | 0.129 | 0.036 | 0.354 | 0.191 | 0.0001 29 | 0.358 | 0.337 | 0.127 | 0.036 | 0.357 | 0.19 | 0.0001 30 | 0.358 | 0.337 | 0.127 | 0.036 | 0.357 | 0.19 | 0.0001 31 | 0.357 | 0.336 | 0.126 | 0.036 | 0.357 | 0.19 | 0.0001 32 | 0.36 | 0.336 | 0.127 | 0.036 | 0.359 | 0.19 | 0.0001 33 | 0.36 | 0.336 | 0.126 | 0.036 | 0.359 | 0.19 | 0.0001 34 | 0.361 | 0.336 | 0.126 | 0.036 | 0.36 | 0.19 | 0.0001 35 | 0.361 | 0.336 | 0.127 | 0.036 | 0.36 | 0.19 | 0.0001 36 | 0.362 | 0.336 | 0.127 | 0.036 | 0.361 | 0.19 | 0.0001 37 | 0.364 | 0.335 | 0.126 | 0.036 | 0.363 | 0.189 | 0.0001 38 | 0.363 | 0.335 | 0.125 | 0.036 | 0.362 | 0.189 | 0.0001 39 | 0.363 | 0.336 | 0.127 | 0.036 | 0.362 | 0.189 | 0.0001 40 | 0.363 | 0.335 | 0.126 | 0.036 | 0.362 | 0.189 | 0.0001 41 | 0.365 | 0.335 | 0.126 | 0.036 | 0.363 | 0.189 | 0.0001 42 | 0.364 | 0.335 | 0.125 | 0.036 | 0.362 | 0.189 | 0.0001 43 | 0.364 | 0.335 | 0.124 | 0.036 | 0.363 | 0.189 | 0.0001 44 | 0.365 | 0.335 | 0.125 | 0.036 | 0.364 | 0.189 | 1e-05 45 | 0.367 | 0.335 | 0.126 | 0.036 | 0.366 | 0.189 | 1e-05 46 | 0.367 | 0.335 | 0.125 | 0.036 | 0.366 | 0.189 | 1e-05 47 | 0.368 | 0.335 | 0.125 | 0.036 | 0.366 | 0.189 | 1e-05 48 | 0.368 | 0.335 | 0.126 | 0.036 | 0.366 | 0.189 | 1e-05 49 | 0.368 | 0.335 | 0.125 | 0.036 | 0.366 | 0.189 | 1e-05 50 | 0 | 0 | 0 | 0 | 0 | 0 | 1e-05 51 | 0.369 | 0.334 | 0.125 | 0.036 | 0.368 | 0.188 | 1e-05 52 | 0.368 | 0.334 | 0.124 | 0.036 | 0.367 | 0.188 | 1e-05 53 | 0.369 | 0.334 | 0.125 | 0.035 | 0.368 | 0.188 | 1e-05 54 | 0.369 | 0.334 | 0.125 | 0.035 | 0.368 | 0.188 | 1e-05 55 | 0.368 | 0.334 | 0.124 | 0.036 | 0.367 | 0.189 | 1e-05 56 | 0.369 | 0.334 | 0.125 | 0.035 | 0.369 | 0.188 | 1e-05 57 | 0.369 | 0.334 | 0.125 | 0.035 | 0.369 | 0.188 | 1e-05 58 | 0.369 | 0.334 | 0.125 | 0.035 | 0.368 | 0.188 | 1e-05 59 | 0.37 | 0.334 | 0.124 | 0.035 | 0.37 | 0.188 | 1e-05 60 | 0.371 | 0.334 | 0.125 | 0.035 | 0.37 | 0.188 | 1e-05 61 | 0.37 | 0.334 | 0.125 | 0.035 | 0.369 | 0.188 | 1e-05 62 | 0.371 | 0.334 | 0.124 | 0.035 | 0.369 | 0.188 | 1e-05 63 | 0.369 | 0.334 | 0.125 | 0.035 | 0.368 | 0.188 | 1e-05 64 | 0.37 | 0.334 | 0.125 | 0.035 | 0.369 | 0.188 | 1e-05 65 | 0.369 | 0.334 | 0.124 | 0.035 | 0.368 | 0.188 | 1e-05 66 | 0.371 | 0.334 | 0.124 | 0.035 | 0.369 | 0.188 | 1e-05 67 | 0.371 | 0.334 | 0.124 | 0.035 | 0.37 | 0.188 | 1.0000000000000002e-06 68 | 0.37 | 0.334 | 0.125 | 0.035 | 0.369 | 0.188 | 1.0000000000000002e-06 69 | 0.371 | 0.334 | 0.126 | 0.035 | 0.369 | 0.188 | 1.0000000000000002e-06 70 | 0.371 | 0.334 | 0.124 | 0.035 | 0.37 | 0.188 | 1.0000000000000002e-06 71 | 0.371 | 0.334 | 0.125 | 0.035 | 0.369 | 0.188 | 1.0000000000000002e-06 72 | 0.37 | 0.334 | 0.124 | 0.035 | 0.37 | 0.188 | 1.0000000000000002e-06 73 | 0.371 | 0.334 | 0.125 | 0.035 | 0.369 | 0.188 | 1.0000000000000002e-06 74 | 0.371 | 0.334 | 0.125 | 0.035 | 0.37 | 0.188 | 1.0000000000000002e-07 75 | 0 | 0 | 0 | 0 | 0 | 0 | 1.0000000000000002e-07 76 | 0.37 | 0.334 | 0.124 | 0.035 | 0.37 | 0.188 | 1.0000000000000002e-07 77 | 0.37 | 0.334 | 0.124 | 0.035 | 0.369 | 0.188 | 1.0000000000000002e-07 --- # CO2 Emissions The estimated CO2 emissions for training this model are documented below: - **Emissions**: 0.13788314685965944 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