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# beit-base-patch16-224-hasta-85-fold5

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6354
- Accuracy: 0.8182

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 1.0103          | 0.5455   |
| No log        | 2.0   | 2    | 0.8046          | 0.6364   |
| No log        | 3.0   | 3    | 0.7378          | 0.7273   |
| No log        | 4.0   | 4    | 1.0676          | 0.7273   |
| No log        | 5.0   | 5    | 1.3956          | 0.7273   |
| No log        | 6.0   | 6    | 1.5799          | 0.7273   |
| No log        | 7.0   | 7    | 1.6061          | 0.7273   |
| No log        | 8.0   | 8    | 1.4351          | 0.7273   |
| No log        | 9.0   | 9    | 1.3296          | 0.7273   |
| 0.39          | 10.0  | 10   | 1.2816          | 0.7273   |
| 0.39          | 11.0  | 11   | 1.2655          | 0.7273   |
| 0.39          | 12.0  | 12   | 1.2040          | 0.7273   |
| 0.39          | 13.0  | 13   | 1.0664          | 0.7273   |
| 0.39          | 14.0  | 14   | 1.0846          | 0.7273   |
| 0.39          | 15.0  | 15   | 1.2145          | 0.7273   |
| 0.39          | 16.0  | 16   | 1.4682          | 0.7273   |
| 0.39          | 17.0  | 17   | 1.4473          | 0.7273   |
| 0.39          | 18.0  | 18   | 1.2699          | 0.7273   |
| 0.39          | 19.0  | 19   | 1.2467          | 0.7273   |
| 0.1832        | 20.0  | 20   | 1.2648          | 0.7273   |
| 0.1832        | 21.0  | 21   | 1.2914          | 0.7273   |
| 0.1832        | 22.0  | 22   | 1.3444          | 0.7273   |
| 0.1832        | 23.0  | 23   | 1.5325          | 0.7273   |
| 0.1832        | 24.0  | 24   | 1.6140          | 0.7273   |
| 0.1832        | 25.0  | 25   | 1.6262          | 0.7273   |
| 0.1832        | 26.0  | 26   | 1.6753          | 0.7273   |
| 0.1832        | 27.0  | 27   | 1.6531          | 0.7273   |
| 0.1832        | 28.0  | 28   | 1.7096          | 0.7273   |
| 0.1832        | 29.0  | 29   | 1.6662          | 0.7273   |
| 0.1194        | 30.0  | 30   | 1.5769          | 0.7273   |
| 0.1194        | 31.0  | 31   | 1.4447          | 0.7273   |
| 0.1194        | 32.0  | 32   | 1.2644          | 0.7273   |
| 0.1194        | 33.0  | 33   | 1.2033          | 0.7273   |
| 0.1194        | 34.0  | 34   | 1.2703          | 0.7273   |
| 0.1194        | 35.0  | 35   | 1.4492          | 0.7273   |
| 0.1194        | 36.0  | 36   | 1.5890          | 0.7273   |
| 0.1194        | 37.0  | 37   | 1.5691          | 0.7273   |
| 0.1194        | 38.0  | 38   | 1.4127          | 0.7273   |
| 0.1194        | 39.0  | 39   | 1.3179          | 0.7273   |
| 0.0783        | 40.0  | 40   | 1.2986          | 0.7273   |
| 0.0783        | 41.0  | 41   | 1.3181          | 0.7273   |
| 0.0783        | 42.0  | 42   | 1.4253          | 0.7273   |
| 0.0783        | 43.0  | 43   | 1.5179          | 0.7273   |
| 0.0783        | 44.0  | 44   | 1.5685          | 0.7273   |
| 0.0783        | 45.0  | 45   | 1.5696          | 0.7273   |
| 0.0783        | 46.0  | 46   | 1.7571          | 0.7273   |
| 0.0783        | 47.0  | 47   | 1.9122          | 0.7273   |
| 0.0783        | 48.0  | 48   | 2.1062          | 0.7273   |
| 0.0783        | 49.0  | 49   | 2.1661          | 0.7273   |
| 0.056         | 50.0  | 50   | 2.1833          | 0.7273   |
| 0.056         | 51.0  | 51   | 2.2402          | 0.7273   |
| 0.056         | 52.0  | 52   | 2.3007          | 0.7273   |
| 0.056         | 53.0  | 53   | 2.3692          | 0.7273   |
| 0.056         | 54.0  | 54   | 2.3821          | 0.7273   |
| 0.056         | 55.0  | 55   | 2.2716          | 0.7273   |
| 0.056         | 56.0  | 56   | 2.0482          | 0.7273   |
| 0.056         | 57.0  | 57   | 1.8783          | 0.7273   |
| 0.056         | 58.0  | 58   | 1.7967          | 0.7273   |
| 0.056         | 59.0  | 59   | 1.7036          | 0.7273   |
| 0.052         | 60.0  | 60   | 1.6389          | 0.7273   |
| 0.052         | 61.0  | 61   | 1.6354          | 0.8182   |
| 0.052         | 62.0  | 62   | 1.6852          | 0.8182   |
| 0.052         | 63.0  | 63   | 1.8189          | 0.7273   |
| 0.052         | 64.0  | 64   | 1.9683          | 0.7273   |
| 0.052         | 65.0  | 65   | 2.0166          | 0.7273   |
| 0.052         | 66.0  | 66   | 2.0855          | 0.7273   |
| 0.052         | 67.0  | 67   | 2.1359          | 0.7273   |
| 0.052         | 68.0  | 68   | 2.2465          | 0.7273   |
| 0.052         | 69.0  | 69   | 2.2680          | 0.7273   |
| 0.0276        | 70.0  | 70   | 2.2728          | 0.7273   |
| 0.0276        | 71.0  | 71   | 2.2820          | 0.7273   |
| 0.0276        | 72.0  | 72   | 2.2427          | 0.7273   |
| 0.0276        | 73.0  | 73   | 2.2066          | 0.7273   |
| 0.0276        | 74.0  | 74   | 2.2434          | 0.7273   |
| 0.0276        | 75.0  | 75   | 2.3206          | 0.7273   |
| 0.0276        | 76.0  | 76   | 2.4408          | 0.7273   |
| 0.0276        | 77.0  | 77   | 2.4810          | 0.7273   |
| 0.0276        | 78.0  | 78   | 2.5091          | 0.7273   |
| 0.0276        | 79.0  | 79   | 2.4862          | 0.7273   |
| 0.0411        | 80.0  | 80   | 2.4502          | 0.7273   |
| 0.0411        | 81.0  | 81   | 2.4204          | 0.7273   |
| 0.0411        | 82.0  | 82   | 2.3838          | 0.7273   |
| 0.0411        | 83.0  | 83   | 2.3431          | 0.7273   |
| 0.0411        | 84.0  | 84   | 2.2927          | 0.7273   |
| 0.0411        | 85.0  | 85   | 2.2181          | 0.7273   |
| 0.0411        | 86.0  | 86   | 2.1633          | 0.7273   |
| 0.0411        | 87.0  | 87   | 2.0966          | 0.7273   |
| 0.0411        | 88.0  | 88   | 2.0536          | 0.7273   |
| 0.0411        | 89.0  | 89   | 2.0427          | 0.7273   |
| 0.0317        | 90.0  | 90   | 2.0524          | 0.7273   |
| 0.0317        | 91.0  | 91   | 2.0489          | 0.7273   |
| 0.0317        | 92.0  | 92   | 2.0648          | 0.7273   |
| 0.0317        | 93.0  | 93   | 2.0946          | 0.7273   |
| 0.0317        | 94.0  | 94   | 2.1155          | 0.7273   |
| 0.0317        | 95.0  | 95   | 2.1469          | 0.7273   |
| 0.0317        | 96.0  | 96   | 2.1768          | 0.7273   |
| 0.0317        | 97.0  | 97   | 2.2026          | 0.7273   |
| 0.0317        | 98.0  | 98   | 2.2205          | 0.7273   |
| 0.0317        | 99.0  | 99   | 2.2304          | 0.7273   |
| 0.0394        | 100.0 | 100  | 2.2350          | 0.7273   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

hafi̇f orta şi̇ddetli̇