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# vit-base-patch16-224-in21k-ISL-final-1.0

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0518
- Accuracy: 0.9938

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.1101        | 1.0   | 193  | 0.9557   | 0.7079          |
| 0.5682        | 2.0   | 386  | 0.9787   | 0.2368          |
| 0.4623        | 3.0   | 579  | 0.9891   | 0.1313          |
| 0.4103        | 4.0   | 772  | 0.9840   | 0.1107          |
| 0.3254        | 5.0   | 965  | 0.9916   | 0.0713          |
| 0.2949        | 6.0   | 1158 | 0.9918   | 0.0570          |
| 0.2638        | 7.0   | 1351 | 0.0518   | 0.9938          |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3

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