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# vit-base-patch16-224-finetuned-cifar10

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: 0.0564
- Accuracy: 0.9844

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4597        | 0.03  | 10   | 2.2902          | 0.1662   |
| 2.1429        | 0.06  | 20   | 1.7855          | 0.5086   |
| 1.6466        | 0.09  | 30   | 1.0829          | 0.8484   |
| 0.9962        | 0.11  | 40   | 0.4978          | 0.9288   |
| 0.6127        | 0.14  | 50   | 0.2717          | 0.9508   |
| 0.4544        | 0.17  | 60   | 0.1942          | 0.9588   |
| 0.4352        | 0.2   | 70   | 0.1504          | 0.9672   |
| 0.374         | 0.23  | 80   | 0.1221          | 0.9718   |
| 0.3261        | 0.26  | 90   | 0.1057          | 0.9772   |
| 0.34          | 0.28  | 100  | 0.0943          | 0.979    |
| 0.284         | 0.31  | 110  | 0.0958          | 0.9754   |
| 0.3151        | 0.34  | 120  | 0.0866          | 0.9776   |
| 0.3004        | 0.37  | 130  | 0.0838          | 0.9788   |
| 0.3334        | 0.4   | 140  | 0.0798          | 0.9806   |
| 0.3018        | 0.43  | 150  | 0.0800          | 0.9778   |
| 0.2957        | 0.45  | 160  | 0.0749          | 0.9808   |
| 0.2952        | 0.48  | 170  | 0.0704          | 0.9814   |
| 0.3084        | 0.51  | 180  | 0.0720          | 0.9812   |
| 0.3015        | 0.54  | 190  | 0.0708          | 0.983    |
| 0.2763        | 0.57  | 200  | 0.0672          | 0.9832   |
| 0.3376        | 0.6   | 210  | 0.0700          | 0.982    |
| 0.285         | 0.63  | 220  | 0.0657          | 0.9828   |
| 0.2857        | 0.65  | 230  | 0.0629          | 0.9836   |
| 0.2644        | 0.68  | 240  | 0.0612          | 0.9842   |
| 0.2461        | 0.71  | 250  | 0.0601          | 0.9836   |
| 0.2802        | 0.74  | 260  | 0.0589          | 0.9842   |
| 0.2481        | 0.77  | 270  | 0.0604          | 0.9838   |
| 0.2641        | 0.8   | 280  | 0.0591          | 0.9846   |
| 0.2737        | 0.82  | 290  | 0.0581          | 0.9842   |
| 0.2391        | 0.85  | 300  | 0.0565          | 0.9852   |
| 0.2283        | 0.88  | 310  | 0.0558          | 0.986    |
| 0.2626        | 0.91  | 320  | 0.0559          | 0.9852   |
| 0.2325        | 0.94  | 330  | 0.0563          | 0.9846   |
| 0.2459        | 0.97  | 340  | 0.0565          | 0.9846   |
| 0.2474        | 1.0   | 350  | 0.0564          | 0.9844   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1

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