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--- |
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language: |
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- eng |
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license: wtfpl |
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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: microsoft/resnet-50 |
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model-index: |
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- name: resnet-50-2024_09_13-batch-size32_epochs150_freeze |
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results: [] |
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--- |
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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: |
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- Loss: nan |
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- F1 Micro: 0.0002 |
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- F1 Macro: 0.0002 |
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- Roc Auc: 0.4995 |
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- Accuracy: 0.0003 |
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--- |
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# Model description |
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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. |
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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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# 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 number of images for each class are given in the following table: |
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| Class | train | val | test | Total | |
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|:-------------------------|--------:|------:|-------:|--------:| |
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| Acropore_branched | 1469 | 464 | 475 | 2408 | |
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| Acropore_digitised | 568 | 160 | 160 | 888 | |
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| Acropore_sub_massive | 150 | 50 | 43 | 243 | |
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| Acropore_tabular | 999 | 297 | 293 | 1589 | |
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| Algae_assembly | 2546 | 847 | 845 | 4238 | |
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| Algae_drawn_up | 367 | 126 | 127 | 620 | |
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| Algae_limestone | 1652 | 557 | 563 | 2772 | |
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| Algae_sodding | 3148 | 984 | 985 | 5117 | |
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| Atra/Leucospilota | 1084 | 348 | 360 | 1792 | |
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| Bleached_coral | 219 | 71 | 70 | 360 | |
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| Blurred | 191 | 67 | 62 | 320 | |
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| Dead_coral | 1979 | 642 | 643 | 3264 | |
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| Fish | 2018 | 656 | 647 | 3321 | |
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| Homo_sapiens | 161 | 62 | 59 | 282 | |
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| Human_object | 157 | 58 | 55 | 270 | |
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| Living_coral | 406 | 154 | 141 | 701 | |
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| Millepore | 385 | 127 | 125 | 637 | |
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| No_acropore_encrusting | 441 | 130 | 154 | 725 | |
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| No_acropore_foliaceous | 204 | 36 | 46 | 286 | |
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| No_acropore_massive | 1031 | 336 | 338 | 1705 | |
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| No_acropore_solitary | 202 | 53 | 48 | 303 | |
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| No_acropore_sub_massive | 1401 | 433 | 422 | 2256 | |
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| Rock | 4489 | 1495 | 1473 | 7457 | |
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| Rubble | 3092 | 1030 | 1001 | 5123 | |
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| Sand | 5842 | 1939 | 1938 | 9719 | |
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| Sea_cucumber | 1408 | 439 | 447 | 2294 | |
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| Sea_urchins | 327 | 107 | 111 | 545 | |
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| Sponge | 269 | 96 | 105 | 470 | |
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| Syringodium_isoetifolium | 1212 | 392 | 391 | 1995 | |
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| Thalassodendron_ciliatum | 782 | 261 | 260 | 1303 | |
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| Useless | 579 | 193 | 193 | 965 | |
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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**: 150 |
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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 | Accuracy | F1 Macro | F1 Micro | Learning Rate |
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--- | --- | --- | --- | --- | --- |
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1 | nan | 0.0 | 0.0 | 0.0 | 0.001 |
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2 | nan | 0.000693000693000693 | 0.00031409501374165687 | 0.00040576181781294376 | 0.001 |
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3 | nan | 0.0017325017325017325 | 0.0007850525985241011 | 0.0010049241282283187 | 0.001 |
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4 | nan | 0.0 | 0.0 | 0.0 | 0.001 |
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5 | nan | 0.0010395010395010396 | 0.00047177229124076113 | 0.0006430178973314757 | 0.001 |
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6 | nan | 0.0003465003465003465 | 0.00015712153350616704 | 0.000206782464846981 | 0.001 |
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7 | nan | 0.0 | 0.0 | 0.0 | 0.0001 |
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8 | nan | 0.0003465003465003465 | 0.00015710919088766695 | 0.0002061218179944347 | 0.0001 |
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9 | nan | 0.0 | 0.0 | 0.0 | 0.0001 |
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10 | nan | 0.000693000693000693 | 0.00031441597233139445 | 0.0004230565838180856 | 0.0001 |
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11 | nan | 0.0 | 0.0 | 0.0 | 0.0001 |
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# CO2 Emissions |
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The estimated CO2 emissions for training this model are documented below: |
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- **Emissions**: 0.12280230273705112 grams of CO2 |
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- **Source**: Code Carbon |
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- **Training Type**: fine-tuning |
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- **Geographical Location**: Brest, France |
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- **Hardware Used**: NVIDIA Tesla V100 PCIe 32 Go |
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# Framework Versions |
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- **Transformers**: 4.41.1 |
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- **Pytorch**: 2.3.0+cu121 |
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- **Datasets**: 2.19.1 |
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- **Tokenizers**: 0.19.1 |
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