melanoma-isic2019-vit384
This model is a fine-tuned version of google/vit-base-patch16-384 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1922
- Auc: 0.9582
- Accuracy: 0.9258
- Recall: 0.7434
- Specificity: 0.9654
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Auc | Accuracy | Recall | Specificity |
|---|---|---|---|---|---|---|---|
| 12.253 | 1.0 | 713 | 0.2653 | 0.9093 | 0.8950 | 0.5863 | 0.9621 |
| 8.333 | 2.0 | 1426 | 0.2467 | 0.9331 | 0.8954 | 0.7279 | 0.9318 |
| 5.4539 | 3.0 | 2139 | 0.1963 | 0.9521 | 0.9219 | 0.7367 | 0.9621 |
| 4.1971 | 4.0 | 2852 | 0.1956 | 0.9563 | 0.9203 | 0.6681 | 0.9750 |
| 2.7437 | 5.0 | 3565 | 0.1922 | 0.9582 | 0.9258 | 0.7434 | 0.9654 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.2
- Tokenizers 0.22.1