--- language: - eng license: wtfpl tags: - multilabel-image-classification - multilabel - generated_from_trainer base_model: facebook/dinov2-large model-index: - name: drone-DinoVdeau-large-2024_07_31-batch-size8_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: 0.3552 - Loss: 0.3286 - MAE: 0.1261 - MSE: 0.0374 - R2: 0.3545 - RMSE: 0.1933 --- # 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 | 2371 | 782 | 785 | 3938 | | Acropore_digitised | 1693 | 580 | 579 | 2852 | | Acropore_sub_massive | 353 | 99 | 97 | 549 | | Acropore_tabular | 1112 | 420 | 410 | 1942 | | Algae | 13150 | 4386 | 4405 | 21941 | | Dead_coral | 6824 | 2242 | 2250 | 11316 | | Millepore | 1543 | 611 | 631 | 2785 | | No_acropore_encrusting | 2799 | 1044 | 1041 | 4884 | | No_acropore_massive | 6578 | 2216 | 2170 | 10964 | | No_acropore_sub_massive | 5252 | 1802 | 1793 | 8847 | | Rock | 13532 | 4529 | 4529 | 22590 | | Rubble | 12641 | 4222 | 4231 | 21094 | | Sand | 13315 | 4438 | 4438 | 22191 | --- # Training procedure ## Training hyperparameters The following hyperparameters were used during training: - **Number of Epochs**: 100 - **Learning Rate**: 0.001 - **Train Batch Size**: 8 - **Eval Batch Size**: 8 - **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 --- | --- | --- | --- | --- | --- | --- | --- 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 1 | 0.233 | 0.353 | 0.135 | 0.045 | 0.228 | 0.212 | 0.001 2 | 0.242 | 0.354 | 0.145 | 0.046 | 0.232 | 0.213 | 0.001 3 | 0.264 | 0.35 | 0.141 | 0.044 | 0.26 | 0.209 | 0.001 4 | 0.273 | 0.347 | 0.14 | 0.043 | 0.271 | 0.208 | 0.001 5 | 0.257 | 0.365 | 0.139 | 0.043 | 0.256 | 0.208 | 0.001 6 | 0.228 | 0.37 | 0.14 | 0.044 | 0.226 | 0.209 | 0.001 7 | 0.28 | 0.357 | 0.137 | 0.042 | 0.278 | 0.206 | 0.001 8 | 0.287 | 0.345 | 0.134 | 0.042 | 0.282 | 0.205 | 0.001 9 | 0.28 | 0.345 | 0.139 | 0.043 | 0.278 | 0.206 | 0.001 10 | 0.282 | 0.386 | 0.133 | 0.043 | 0.279 | 0.206 | 0.001 11 | 0.28 | 0.347 | 0.137 | 0.043 | 0.276 | 0.207 | 0.001 12 | 0.285 | 0.351 | 0.137 | 0.042 | 0.28 | 0.206 | 0.001 13 | 0.277 | 0.397 | 0.136 | 0.043 | 0.275 | 0.207 | 0.001 14 | 0.276 | 0.359 | 0.137 | 0.043 | 0.271 | 0.208 | 0.001 15 | 0.276 | 0.349 | 0.136 | 0.042 | 0.274 | 0.206 | 0.001 16 | 0.303 | 0.339 | 0.133 | 0.041 | 0.303 | 0.202 | 0.0001 17 | 0.306 | 0.338 | 0.134 | 0.04 | 0.306 | 0.201 | 0.0001 18 | 0.31 | 0.337 | 0.132 | 0.04 | 0.308 | 0.2 | 0.0001 19 | 0.307 | 0.338 | 0.131 | 0.04 | 0.305 | 0.201 | 0.0001 20 | 0.312 | 0.336 | 0.131 | 0.04 | 0.311 | 0.2 | 0.0001 21 | 0.312 | 0.337 | 0.129 | 0.04 | 0.308 | 0.2 | 0.0001 22 | 0.316 | 0.336 | 0.132 | 0.04 | 0.315 | 0.199 | 0.0001 23 | 0.32 | 0.335 | 0.131 | 0.039 | 0.319 | 0.199 | 0.0001 24 | 0.319 | 0.335 | 0.13 | 0.04 | 0.318 | 0.199 | 0.0001 25 | 0.324 | 0.334 | 0.13 | 0.039 | 0.323 | 0.198 | 0.0001 26 | 0.32 | 0.335 | 0.13 | 0.04 | 0.318 | 0.199 | 0.0001 27 | 0.321 | 0.335 | 0.13 | 0.039 | 0.32 | 0.198 | 0.0001 28 | 0.326 | 0.334 | 0.127 | 0.039 | 0.321 | 0.198 | 0.0001 29 | 0.33 | 0.333 | 0.129 | 0.039 | 0.328 | 0.197 | 0.0001 30 | 0.33 | 0.333 | 0.13 | 0.039 | 0.33 | 0.197 | 0.0001 31 | 0.328 | 0.333 | 0.13 | 0.039 | 0.325 | 0.198 | 0.0001 32 | 0.331 | 0.332 | 0.128 | 0.039 | 0.33 | 0.197 | 0.0001 33 | 0.334 | 0.333 | 0.13 | 0.039 | 0.331 | 0.196 | 0.0001 34 | 0.33 | 0.333 | 0.129 | 0.039 | 0.328 | 0.197 | 0.0001 35 | 0.325 | 0.334 | 0.131 | 0.039 | 0.324 | 0.198 | 0.0001 36 | 0.337 | 0.332 | 0.13 | 0.038 | 0.337 | 0.196 | 0.0001 37 | 0.328 | 0.334 | 0.13 | 0.039 | 0.327 | 0.197 | 0.0001 38 | 0.338 | 0.332 | 0.129 | 0.038 | 0.336 | 0.196 | 0.0001 39 | 0.338 | 0.332 | 0.128 | 0.038 | 0.338 | 0.196 | 0.0001 40 | 0.337 | 0.332 | 0.129 | 0.038 | 0.336 | 0.196 | 0.0001 41 | 0.335 | 0.333 | 0.131 | 0.039 | 0.333 | 0.196 | 0.0001 42 | 0.338 | 0.332 | 0.129 | 0.038 | 0.337 | 0.196 | 0.0001 43 | 0.338 | 0.331 | 0.129 | 0.038 | 0.338 | 0.196 | 0.0001 44 | 0.336 | 0.333 | 0.128 | 0.039 | 0.335 | 0.196 | 0.0001 45 | 0.339 | 0.331 | 0.128 | 0.038 | 0.338 | 0.196 | 0.0001 46 | 0.341 | 0.332 | 0.129 | 0.038 | 0.339 | 0.195 | 0.0001 47 | 0.34 | 0.331 | 0.127 | 0.038 | 0.339 | 0.196 | 0.0001 48 | 0.299 | 0.339 | 0.131 | 0.039 | 0.295 | 0.199 | 0.0001 49 | 0.338 | 0.331 | 0.128 | 0.038 | 0.337 | 0.196 | 0.0001 50 | 0.342 | 0.332 | 0.127 | 0.038 | 0.339 | 0.196 | 0.0001 51 | 0.341 | 0.331 | 0.127 | 0.038 | 0.341 | 0.195 | 0.0001 52 | 0.345 | 0.33 | 0.127 | 0.038 | 0.344 | 0.195 | 0.0001 53 | 0.34 | 0.331 | 0.128 | 0.038 | 0.339 | 0.196 | 0.0001 54 | 0.341 | 0.331 | 0.129 | 0.038 | 0.34 | 0.196 | 0.0001 55 | 0.349 | 0.329 | 0.127 | 0.038 | 0.349 | 0.194 | 0.0001 56 | 0.344 | 0.33 | 0.126 | 0.038 | 0.343 | 0.195 | 0.0001 57 | 0.341 | 0.331 | 0.126 | 0.038 | 0.339 | 0.196 | 0.0001 58 | 0.348 | 0.33 | 0.126 | 0.038 | 0.347 | 0.194 | 0.0001 59 | 0.343 | 0.332 | 0.128 | 0.038 | 0.341 | 0.195 | 0.0001 60 | 0.346 | 0.331 | 0.128 | 0.038 | 0.345 | 0.195 | 0.0001 61 | 0.346 | 0.33 | 0.125 | 0.038 | 0.344 | 0.195 | 0.0001 62 | 0.347 | 0.329 | 0.126 | 0.038 | 0.346 | 0.194 | 1e-05 63 | 0.35 | 0.33 | 0.128 | 0.038 | 0.348 | 0.194 | 1e-05 64 | 0.345 | 0.33 | 0.126 | 0.038 | 0.344 | 0.195 | 1e-05 65 | 0.349 | 0.33 | 0.128 | 0.038 | 0.347 | 0.195 | 1e-05 --- # CO2 Emissions The estimated CO2 emissions for training this model are documented below: - **Emissions**: 0.19095786836275294 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