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# vit-base-patch16-224-in21k

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6306
- Accuracy: 0.5375

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 40   | 1.2472          | 0.5312   |
| No log        | 2.0   | 80   | 1.2878          | 0.5188   |
| No log        | 3.0   | 120  | 1.3116          | 0.525    |
| No log        | 4.0   | 160  | 1.2578          | 0.55     |
| No log        | 5.0   | 200  | 1.2186          | 0.5563   |
| No log        | 6.0   | 240  | 1.2680          | 0.5563   |
| No log        | 7.0   | 280  | 1.3674          | 0.5      |
| No log        | 8.0   | 320  | 1.3814          | 0.525    |
| No log        | 9.0   | 360  | 1.4394          | 0.5      |
| No log        | 10.0  | 400  | 1.3710          | 0.5437   |
| No log        | 11.0  | 440  | 1.3721          | 0.5437   |
| No log        | 12.0  | 480  | 1.4309          | 0.5563   |
| 0.4861        | 13.0  | 520  | 1.3424          | 0.575    |
| 0.4861        | 14.0  | 560  | 1.4617          | 0.525    |
| 0.4861        | 15.0  | 600  | 1.3964          | 0.5813   |
| 0.4861        | 16.0  | 640  | 1.4751          | 0.5687   |
| 0.4861        | 17.0  | 680  | 1.5296          | 0.55     |
| 0.4861        | 18.0  | 720  | 1.5887          | 0.5188   |
| 0.4861        | 19.0  | 760  | 1.5784          | 0.5312   |
| 0.4861        | 20.0  | 800  | 1.7036          | 0.5375   |
| 0.4861        | 21.0  | 840  | 1.6988          | 0.5188   |
| 0.4861        | 22.0  | 880  | 1.6070          | 0.5687   |
| 0.4861        | 23.0  | 920  | 1.7111          | 0.55     |
| 0.4861        | 24.0  | 960  | 1.6730          | 0.55     |
| 0.2042        | 25.0  | 1000 | 1.6559          | 0.55     |
| 0.2042        | 26.0  | 1040 | 1.7221          | 0.5563   |
| 0.2042        | 27.0  | 1080 | 1.6637          | 0.5813   |
| 0.2042        | 28.0  | 1120 | 1.6806          | 0.5625   |
| 0.2042        | 29.0  | 1160 | 1.5743          | 0.5938   |
| 0.2042        | 30.0  | 1200 | 1.7899          | 0.4938   |
| 0.2042        | 31.0  | 1240 | 1.7422          | 0.5312   |
| 0.2042        | 32.0  | 1280 | 1.7712          | 0.55     |
| 0.2042        | 33.0  | 1320 | 1.7480          | 0.5188   |
| 0.2042        | 34.0  | 1360 | 1.7964          | 0.5375   |
| 0.2042        | 35.0  | 1400 | 1.9687          | 0.5188   |
| 0.2042        | 36.0  | 1440 | 1.7412          | 0.5813   |
| 0.2042        | 37.0  | 1480 | 1.9312          | 0.4875   |
| 0.1342        | 38.0  | 1520 | 1.7944          | 0.525    |
| 0.1342        | 39.0  | 1560 | 1.8180          | 0.55     |
| 0.1342        | 40.0  | 1600 | 1.7720          | 0.5563   |
| 0.1342        | 41.0  | 1640 | 1.9014          | 0.5312   |
| 0.1342        | 42.0  | 1680 | 1.7519          | 0.55     |
| 0.1342        | 43.0  | 1720 | 1.9793          | 0.5      |
| 0.1342        | 44.0  | 1760 | 1.8642          | 0.55     |
| 0.1342        | 45.0  | 1800 | 1.7573          | 0.5875   |
| 0.1342        | 46.0  | 1840 | 1.8508          | 0.5125   |
| 0.1342        | 47.0  | 1880 | 1.9741          | 0.5625   |
| 0.1342        | 48.0  | 1920 | 1.9012          | 0.525    |
| 0.1342        | 49.0  | 1960 | 1.8771          | 0.5625   |
| 0.0926        | 50.0  | 2000 | 1.8728          | 0.5125   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

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