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# vit-base-patch16-224-finetuned-imageclassification

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1790
- Accuracy: 0.9502

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 9    | 0.5791          | 0.9004   |
| 1.4122        | 2.0   | 18   | 0.2002          | 0.9359   |
| 0.3147        | 3.0   | 27   | 0.1717          | 0.9502   |
| 0.1907        | 4.0   | 36   | 0.1632          | 0.9466   |
| 0.158         | 5.0   | 45   | 0.1822          | 0.9466   |
| 0.1169        | 6.0   | 54   | 0.1778          | 0.9502   |
| 0.0984        | 7.0   | 63   | 0.1552          | 0.9573   |
| 0.0971        | 8.0   | 72   | 0.1835          | 0.9502   |
| 0.0965        | 9.0   | 81   | 0.1878          | 0.9484   |
| 0.0766        | 10.0  | 90   | 0.1790          | 0.9502   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1

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