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# vit-base-16-thesis-demo-ISIC-multi-class

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/isic_enhanced_dec_balanced dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0906
- Accuracy: 0.9748
- Recall: 0.9748
- F1: 0.9748
- Precision: 0.9748

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.575         | 0.98  | 50   | 0.4132          | 0.8491   | 0.8491 | 0.8491 | 0.8491    |
| 0.2771        | 1.96  | 100  | 0.2329          | 0.9182   | 0.9182 | 0.9182 | 0.9182    |
| 0.1703        | 2.94  | 150  | 0.1821          | 0.9497   | 0.9497 | 0.9497 | 0.9497    |
| 0.1186        | 3.92  | 200  | 0.0906          | 0.9748   | 0.9748 | 0.9748 | 0.9748    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy", "recall", "f1", "precision"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-base-16-thesis-demo-ISIC-multi-class", "results": []}]}
transformers tensorboard safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-224-in21k license:apache-2.0 autotrain_compatible endpoints_compatible region:us