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# vit-base-patch16-224-in21k-finetuned-final-2.0

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: 0.0445
- Accuracy: 0.9895

## 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: 16

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.6672        | 0.9950  | 100  | 0.4986          | 0.9233   |
| 0.3891        | 2.0     | 201  | 0.2037          | 0.9671   |
| 0.3164        | 2.9950  | 301  | 0.1414          | 0.9751   |
| 0.2884        | 4.0     | 402  | 0.1310          | 0.9699   |
| 0.2726        | 4.9950  | 502  | 0.1040          | 0.9748   |
| 0.2595        | 6.0     | 603  | 0.1150          | 0.9709   |
| 0.2662        | 6.9950  | 703  | 0.0842          | 0.9783   |
| 0.2195        | 8.0     | 804  | 0.0704          | 0.9842   |
| 0.2326        | 8.9950  | 904  | 0.0605          | 0.9842   |
| 0.2292        | 10.0    | 1005 | 0.0568          | 0.9846   |
| 0.2229        | 10.9950 | 1105 | 0.0445          | 0.9895   |
| 0.239         | 12.0    | 1206 | 0.0539          | 0.9839   |
| 0.2115        | 12.9950 | 1306 | 0.0464          | 0.9874   |
| 0.22          | 14.0    | 1407 | 0.0818          | 0.9734   |
| 0.2138        | 14.9950 | 1507 | 0.0599          | 0.9790   |
| 0.2003        | 15.9204 | 1600 | 0.0554          | 0.9821   |


### Framework versions

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

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