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# vit-base-patch16-224-in21k-finetuned-earlystop

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: 0.1972
- Accuracy: 0.9375

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use 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: 20

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5989        | 0.9816 | 40   | 0.6929          | 0.5      |
| 0.3542        | 1.9877 | 81   | 0.5951          | 0.6875   |
| 0.2495        | 2.9939 | 122  | 0.5182          | 0.75     |
| 0.1553        | 4.0    | 163  | 0.7023          | 0.625    |
| 0.1806        | 4.9816 | 203  | 0.3825          | 0.8125   |
| 0.1509        | 5.9877 | 244  | 0.1972          | 0.9375   |
| 0.1771        | 6.9939 | 285  | 0.6752          | 0.625    |
| 0.1372        | 8.0    | 326  | 0.4901          | 0.6875   |
| 0.1698        | 8.9816 | 366  | 0.2187          | 0.875    |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3

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