| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224-in21k |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: skin-lesion-vit-finetuned |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # skin-lesion-vit-finetuned |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1761 |
| - Accuracy: 0.9458 |
| - F1 Macro: 0.9465 |
| - Melanoma Recall: 0.9607 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 8 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Melanoma Recall | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:---------------:| |
| | 0.7039 | 0.9983 | 299 | 0.7625 | 0.7335 | 0.6859 | 0.6 | |
| | 0.7119 | 2.0 | 599 | 0.4936 | 0.8327 | 0.7772 | 0.7536 | |
| | 0.4416 | 2.9983 | 898 | 0.4881 | 0.8287 | 0.8242 | 0.8036 | |
| | 0.3581 | 4.0 | 1198 | 0.4164 | 0.8616 | 0.8286 | 0.8929 | |
| | 0.2949 | 4.9983 | 1497 | 0.3000 | 0.9021 | 0.8916 | 0.8893 | |
| | 0.2687 | 6.0 | 1797 | 0.2685 | 0.9041 | 0.9206 | 0.95 | |
| | 0.1699 | 6.9983 | 2096 | 0.2058 | 0.9318 | 0.9346 | 0.9643 | |
| | 0.0908 | 7.9866 | 2392 | 0.1761 | 0.9458 | 0.9465 | 0.9607 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.44.2 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 2.21.0 |
| - Tokenizers 0.19.1 |
|
|
