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# vit-base-beans

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/HAM_db_enhanced_balanced_reduced_50_20_20_50 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5305
- Accuracy: 0.8451

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0791        | 0.2304 | 100  | 1.0348          | 0.6335   |
| 0.9415        | 0.4608 | 200  | 0.9576          | 0.6449   |
| 0.7839        | 0.6912 | 300  | 0.8963          | 0.6662   |
| 0.7181        | 0.9217 | 400  | 0.8479          | 0.6963   |
| 0.3995        | 1.1521 | 500  | 0.7821          | 0.7170   |
| 0.5025        | 1.3825 | 600  | 0.6300          | 0.7837   |
| 0.4985        | 1.6129 | 700  | 0.7059          | 0.7490   |
| 0.4388        | 1.8433 | 800  | 0.5893          | 0.7857   |
| 0.2389        | 2.0737 | 900  | 0.5929          | 0.8077   |
| 0.2767        | 2.3041 | 1000 | 0.5795          | 0.8091   |
| 0.2387        | 2.5346 | 1100 | 0.6100          | 0.8091   |
| 0.1691        | 2.7650 | 1200 | 0.6175          | 0.8071   |
| 0.1738        | 2.9954 | 1300 | 0.5877          | 0.8198   |
| 0.0397        | 3.2258 | 1400 | 0.5766          | 0.8358   |
| 0.03          | 3.4562 | 1500 | 0.5681          | 0.8371   |
| 0.092         | 3.6866 | 1600 | 0.5305          | 0.8451   |
| 0.0416        | 3.9171 | 1700 | 0.5443          | 0.8471   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

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