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# beit-base-patch16-224-hasta-75-fold2

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: 0.0769
- Accuracy: 1.0

## 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    | 0.5149          | 0.9167   |
| No log        | 2.0   | 2    | 0.4041          | 0.9167   |
| No log        | 3.0   | 3    | 0.3602          | 0.9167   |
| No log        | 4.0   | 4    | 0.3914          | 0.9167   |
| No log        | 5.0   | 5    | 0.4230          | 0.9167   |
| No log        | 6.0   | 6    | 0.4505          | 0.9167   |
| No log        | 7.0   | 7    | 0.4577          | 0.9167   |
| No log        | 8.0   | 8    | 0.4562          | 0.9167   |
| No log        | 9.0   | 9    | 0.4353          | 0.9167   |
| 0.2449        | 10.0  | 10   | 0.4424          | 0.9167   |
| 0.2449        | 11.0  | 11   | 0.4563          | 0.9167   |
| 0.2449        | 12.0  | 12   | 0.4720          | 0.8333   |
| 0.2449        | 13.0  | 13   | 0.3685          | 0.9167   |
| 0.2449        | 14.0  | 14   | 0.3112          | 0.9167   |
| 0.2449        | 15.0  | 15   | 0.2517          | 0.9167   |
| 0.2449        | 16.0  | 16   | 0.2199          | 0.9167   |
| 0.2449        | 17.0  | 17   | 0.2582          | 0.9167   |
| 0.2449        | 18.0  | 18   | 0.3222          | 0.9167   |
| 0.2449        | 19.0  | 19   | 0.2529          | 0.9167   |
| 0.1293        | 20.0  | 20   | 0.1724          | 0.9167   |
| 0.1293        | 21.0  | 21   | 0.1393          | 0.9167   |
| 0.1293        | 22.0  | 22   | 0.0965          | 0.9167   |
| 0.1293        | 23.0  | 23   | 0.0769          | 1.0      |
| 0.1293        | 24.0  | 24   | 0.0690          | 1.0      |
| 0.1293        | 25.0  | 25   | 0.1172          | 0.9167   |
| 0.1293        | 26.0  | 26   | 0.1629          | 0.9167   |
| 0.1293        | 27.0  | 27   | 0.1103          | 0.9167   |
| 0.1293        | 28.0  | 28   | 0.1193          | 1.0      |
| 0.1293        | 29.0  | 29   | 0.1003          | 1.0      |
| 0.0785        | 30.0  | 30   | 0.0719          | 1.0      |
| 0.0785        | 31.0  | 31   | 0.1636          | 0.9167   |
| 0.0785        | 32.0  | 32   | 0.2265          | 0.9167   |
| 0.0785        | 33.0  | 33   | 0.2009          | 0.9167   |
| 0.0785        | 34.0  | 34   | 0.1122          | 0.9167   |
| 0.0785        | 35.0  | 35   | 0.0424          | 1.0      |
| 0.0785        | 36.0  | 36   | 0.0407          | 1.0      |
| 0.0785        | 37.0  | 37   | 0.0322          | 1.0      |
| 0.0785        | 38.0  | 38   | 0.0397          | 1.0      |
| 0.0785        | 39.0  | 39   | 0.0569          | 1.0      |
| 0.0519        | 40.0  | 40   | 0.0891          | 0.9167   |
| 0.0519        | 41.0  | 41   | 0.0974          | 0.9167   |
| 0.0519        | 42.0  | 42   | 0.0969          | 0.9167   |
| 0.0519        | 43.0  | 43   | 0.1268          | 0.9167   |
| 0.0519        | 44.0  | 44   | 0.1718          | 0.9167   |
| 0.0519        | 45.0  | 45   | 0.2412          | 0.9167   |
| 0.0519        | 46.0  | 46   | 0.2935          | 0.9167   |
| 0.0519        | 47.0  | 47   | 0.3791          | 0.9167   |
| 0.0519        | 48.0  | 48   | 0.4504          | 0.9167   |
| 0.0519        | 49.0  | 49   | 0.4738          | 0.9167   |
| 0.0542        | 50.0  | 50   | 0.4470          | 0.9167   |
| 0.0542        | 51.0  | 51   | 0.3819          | 0.9167   |
| 0.0542        | 52.0  | 52   | 0.3327          | 0.9167   |
| 0.0542        | 53.0  | 53   | 0.2756          | 0.9167   |
| 0.0542        | 54.0  | 54   | 0.2012          | 0.9167   |
| 0.0542        | 55.0  | 55   | 0.1548          | 0.9167   |
| 0.0542        | 56.0  | 56   | 0.1668          | 0.9167   |
| 0.0542        | 57.0  | 57   | 0.2124          | 0.9167   |
| 0.0542        | 58.0  | 58   | 0.2389          | 0.9167   |
| 0.0542        | 59.0  | 59   | 0.2337          | 0.9167   |
| 0.0223        | 60.0  | 60   | 0.2025          | 0.9167   |
| 0.0223        | 61.0  | 61   | 0.1676          | 0.9167   |
| 0.0223        | 62.0  | 62   | 0.1085          | 0.9167   |
| 0.0223        | 63.0  | 63   | 0.0652          | 0.9167   |
| 0.0223        | 64.0  | 64   | 0.0644          | 0.9167   |
| 0.0223        | 65.0  | 65   | 0.0544          | 1.0      |
| 0.0223        | 66.0  | 66   | 0.0577          | 1.0      |
| 0.0223        | 67.0  | 67   | 0.0799          | 0.9167   |
| 0.0223        | 68.0  | 68   | 0.1005          | 0.9167   |
| 0.0223        | 69.0  | 69   | 0.1069          | 0.9167   |
| 0.0255        | 70.0  | 70   | 0.1410          | 0.9167   |
| 0.0255        | 71.0  | 71   | 0.1748          | 0.9167   |
| 0.0255        | 72.0  | 72   | 0.2266          | 0.9167   |
| 0.0255        | 73.0  | 73   | 0.2680          | 0.9167   |
| 0.0255        | 74.0  | 74   | 0.2917          | 0.9167   |
| 0.0255        | 75.0  | 75   | 0.3008          | 0.9167   |
| 0.0255        | 76.0  | 76   | 0.2897          | 0.9167   |
| 0.0255        | 77.0  | 77   | 0.2652          | 0.9167   |
| 0.0255        | 78.0  | 78   | 0.2542          | 0.9167   |
| 0.0255        | 79.0  | 79   | 0.2405          | 0.9167   |
| 0.0252        | 80.0  | 80   | 0.2391          | 0.9167   |
| 0.0252        | 81.0  | 81   | 0.2155          | 0.9167   |
| 0.0252        | 82.0  | 82   | 0.1953          | 0.9167   |
| 0.0252        | 83.0  | 83   | 0.1830          | 0.9167   |
| 0.0252        | 84.0  | 84   | 0.1672          | 0.9167   |
| 0.0252        | 85.0  | 85   | 0.1694          | 0.9167   |
| 0.0252        | 86.0  | 86   | 0.1743          | 0.9167   |
| 0.0252        | 87.0  | 87   | 0.1845          | 0.9167   |
| 0.0252        | 88.0  | 88   | 0.2098          | 0.9167   |
| 0.0252        | 89.0  | 89   | 0.2427          | 0.9167   |
| 0.0256        | 90.0  | 90   | 0.2682          | 0.9167   |
| 0.0256        | 91.0  | 91   | 0.2838          | 0.9167   |
| 0.0256        | 92.0  | 92   | 0.2936          | 0.9167   |
| 0.0256        | 93.0  | 93   | 0.3037          | 0.9167   |
| 0.0256        | 94.0  | 94   | 0.3065          | 0.9167   |
| 0.0256        | 95.0  | 95   | 0.3058          | 0.9167   |
| 0.0256        | 96.0  | 96   | 0.2997          | 0.9167   |
| 0.0256        | 97.0  | 97   | 0.2920          | 0.9167   |
| 0.0256        | 98.0  | 98   | 0.2849          | 0.9167   |
| 0.0256        | 99.0  | 99   | 0.2810          | 0.9167   |
| 0.0279        | 100.0 | 100  | 0.2798          | 0.9167   |


### Framework versions

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

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