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# Chess_Images

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: 0.2460
- Accuracy: 0.9333

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 2    | 0.3365          | 0.9333   |
| No log        | 2.0   | 4    | 0.3018          | 0.9333   |
| No log        | 3.0   | 6    | 0.3443          | 0.9667   |
| No log        | 4.0   | 8    | 0.2189          | 1.0      |
| 0.213         | 5.0   | 10   | 0.3188          | 0.9667   |
| 0.213         | 6.0   | 12   | 0.2903          | 0.9333   |
| 0.213         | 7.0   | 14   | 0.3398          | 0.9      |
| 0.213         | 8.0   | 16   | 0.3879          | 0.8667   |
| 0.213         | 9.0   | 18   | 0.3023          | 0.9333   |
| 0.2116        | 10.0  | 20   | 0.1857          | 1.0      |
| 0.2116        | 11.0  | 22   | 0.2737          | 0.9667   |
| 0.2116        | 12.0  | 24   | 0.2675          | 1.0      |
| 0.2116        | 13.0  | 26   | 0.2817          | 0.9333   |
| 0.2116        | 14.0  | 28   | 0.4394          | 0.8667   |
| 0.1837        | 15.0  | 30   | 0.3167          | 0.9      |
| 0.1837        | 16.0  | 32   | 0.2795          | 0.9333   |
| 0.1837        | 17.0  | 34   | 0.2315          | 0.9333   |
| 0.1837        | 18.0  | 36   | 0.2266          | 0.9667   |
| 0.1837        | 19.0  | 38   | 0.3199          | 0.9333   |
| 0.1726        | 20.0  | 40   | 0.2553          | 0.9667   |
| 0.1726        | 21.0  | 42   | 0.3804          | 0.9      |
| 0.1726        | 22.0  | 44   | 0.2118          | 0.9667   |
| 0.1726        | 23.0  | 46   | 0.1784          | 1.0      |
| 0.1726        | 24.0  | 48   | 0.2098          | 0.9667   |
| 0.1529        | 25.0  | 50   | 0.1676          | 1.0      |
| 0.1529        | 26.0  | 52   | 0.2980          | 0.9      |
| 0.1529        | 27.0  | 54   | 0.2726          | 0.9667   |
| 0.1529        | 28.0  | 56   | 0.1756          | 1.0      |
| 0.1529        | 29.0  | 58   | 0.2266          | 0.9667   |
| 0.1335        | 30.0  | 60   | 0.3161          | 0.9333   |
| 0.1335        | 31.0  | 62   | 0.2872          | 0.9333   |
| 0.1335        | 32.0  | 64   | 0.2030          | 1.0      |
| 0.1335        | 33.0  | 66   | 0.2297          | 0.9333   |
| 0.1335        | 34.0  | 68   | 0.2876          | 0.9333   |
| 0.1228        | 35.0  | 70   | 0.1432          | 1.0      |
| 0.1228        | 36.0  | 72   | 0.2194          | 0.9667   |
| 0.1228        | 37.0  | 74   | 0.1387          | 1.0      |
| 0.1228        | 38.0  | 76   | 0.1381          | 1.0      |
| 0.1228        | 39.0  | 78   | 0.1540          | 1.0      |
| 0.1324        | 40.0  | 80   | 0.3075          | 0.8667   |
| 0.1324        | 41.0  | 82   | 0.1892          | 1.0      |
| 0.1324        | 42.0  | 84   | 0.1487          | 1.0      |
| 0.1324        | 43.0  | 86   | 0.1515          | 1.0      |
| 0.1324        | 44.0  | 88   | 0.2617          | 0.9333   |
| 0.136         | 45.0  | 90   | 0.1719          | 0.9667   |
| 0.136         | 46.0  | 92   | 0.2501          | 0.9      |
| 0.136         | 47.0  | 94   | 0.1618          | 1.0      |
| 0.136         | 48.0  | 96   | 0.2175          | 0.9667   |
| 0.136         | 49.0  | 98   | 0.2039          | 0.9667   |
| 0.1226        | 50.0  | 100  | 0.2460          | 0.9333   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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