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# beit-base-patch16-224-hasta-85-fold3

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: 0.8911
- Accuracy: 0.7273

## 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    | 1.3301          | 0.1818   |
| No log        | 2.0   | 2    | 1.1012          | 0.3636   |
| No log        | 3.0   | 3    | 0.8911          | 0.7273   |
| No log        | 4.0   | 4    | 0.9555          | 0.7273   |
| No log        | 5.0   | 5    | 1.2582          | 0.7273   |
| No log        | 6.0   | 6    | 1.5576          | 0.7273   |
| No log        | 7.0   | 7    | 1.6687          | 0.7273   |
| No log        | 8.0   | 8    | 1.5015          | 0.7273   |
| No log        | 9.0   | 9    | 1.2729          | 0.7273   |
| 0.3584        | 10.0  | 10   | 1.2127          | 0.7273   |
| 0.3584        | 11.0  | 11   | 1.3132          | 0.7273   |
| 0.3584        | 12.0  | 12   | 1.3350          | 0.7273   |
| 0.3584        | 13.0  | 13   | 1.2579          | 0.7273   |
| 0.3584        | 14.0  | 14   | 1.3559          | 0.7273   |
| 0.3584        | 15.0  | 15   | 1.4231          | 0.7273   |
| 0.3584        | 16.0  | 16   | 1.5141          | 0.7273   |
| 0.3584        | 17.0  | 17   | 1.4200          | 0.7273   |
| 0.3584        | 18.0  | 18   | 1.2498          | 0.7273   |
| 0.3584        | 19.0  | 19   | 1.1456          | 0.7273   |
| 0.1919        | 20.0  | 20   | 1.1055          | 0.7273   |
| 0.1919        | 21.0  | 21   | 1.1937          | 0.7273   |
| 0.1919        | 22.0  | 22   | 1.2768          | 0.7273   |
| 0.1919        | 23.0  | 23   | 1.3224          | 0.7273   |
| 0.1919        | 24.0  | 24   | 1.3629          | 0.7273   |
| 0.1919        | 25.0  | 25   | 1.3238          | 0.7273   |
| 0.1919        | 26.0  | 26   | 1.2280          | 0.7273   |
| 0.1919        | 27.0  | 27   | 1.2446          | 0.7273   |
| 0.1919        | 28.0  | 28   | 1.2530          | 0.7273   |
| 0.1919        | 29.0  | 29   | 1.2468          | 0.7273   |
| 0.1447        | 30.0  | 30   | 1.1535          | 0.7273   |
| 0.1447        | 31.0  | 31   | 1.1125          | 0.7273   |
| 0.1447        | 32.0  | 32   | 1.2051          | 0.7273   |
| 0.1447        | 33.0  | 33   | 1.5902          | 0.7273   |
| 0.1447        | 34.0  | 34   | 1.8445          | 0.7273   |
| 0.1447        | 35.0  | 35   | 1.7222          | 0.7273   |
| 0.1447        | 36.0  | 36   | 1.5080          | 0.7273   |
| 0.1447        | 37.0  | 37   | 1.3542          | 0.7273   |
| 0.1447        | 38.0  | 38   | 1.3106          | 0.7273   |
| 0.1447        | 39.0  | 39   | 1.4533          | 0.7273   |
| 0.1053        | 40.0  | 40   | 1.6427          | 0.7273   |
| 0.1053        | 41.0  | 41   | 1.7518          | 0.7273   |
| 0.1053        | 42.0  | 42   | 1.7775          | 0.7273   |
| 0.1053        | 43.0  | 43   | 1.6831          | 0.7273   |
| 0.1053        | 44.0  | 44   | 1.6968          | 0.7273   |
| 0.1053        | 45.0  | 45   | 1.8236          | 0.7273   |
| 0.1053        | 46.0  | 46   | 1.8845          | 0.7273   |
| 0.1053        | 47.0  | 47   | 1.8785          | 0.7273   |
| 0.1053        | 48.0  | 48   | 1.8805          | 0.7273   |
| 0.1053        | 49.0  | 49   | 1.9625          | 0.7273   |
| 0.0771        | 50.0  | 50   | 1.9860          | 0.7273   |
| 0.0771        | 51.0  | 51   | 1.9708          | 0.7273   |
| 0.0771        | 52.0  | 52   | 1.9149          | 0.7273   |
| 0.0771        | 53.0  | 53   | 1.9064          | 0.7273   |
| 0.0771        | 54.0  | 54   | 1.8804          | 0.7273   |
| 0.0771        | 55.0  | 55   | 1.8467          | 0.7273   |
| 0.0771        | 56.0  | 56   | 1.8508          | 0.7273   |
| 0.0771        | 57.0  | 57   | 1.8675          | 0.7273   |
| 0.0771        | 58.0  | 58   | 1.8886          | 0.7273   |
| 0.0771        | 59.0  | 59   | 1.8860          | 0.7273   |
| 0.0528        | 60.0  | 60   | 1.8777          | 0.7273   |
| 0.0528        | 61.0  | 61   | 1.9119          | 0.7273   |
| 0.0528        | 62.0  | 62   | 1.9860          | 0.7273   |
| 0.0528        | 63.0  | 63   | 2.1003          | 0.7273   |
| 0.0528        | 64.0  | 64   | 2.1561          | 0.7273   |
| 0.0528        | 65.0  | 65   | 2.1454          | 0.7273   |
| 0.0528        | 66.0  | 66   | 2.0685          | 0.7273   |
| 0.0528        | 67.0  | 67   | 1.9261          | 0.7273   |
| 0.0528        | 68.0  | 68   | 1.6839          | 0.7273   |
| 0.0528        | 69.0  | 69   | 1.4306          | 0.7273   |
| 0.043         | 70.0  | 70   | 1.3800          | 0.7273   |
| 0.043         | 71.0  | 71   | 1.4814          | 0.7273   |
| 0.043         | 72.0  | 72   | 1.6014          | 0.7273   |
| 0.043         | 73.0  | 73   | 1.7792          | 0.7273   |
| 0.043         | 74.0  | 74   | 1.9423          | 0.7273   |
| 0.043         | 75.0  | 75   | 2.0590          | 0.7273   |
| 0.043         | 76.0  | 76   | 2.1119          | 0.7273   |
| 0.043         | 77.0  | 77   | 2.1116          | 0.7273   |
| 0.043         | 78.0  | 78   | 2.0979          | 0.7273   |
| 0.043         | 79.0  | 79   | 2.1457          | 0.7273   |
| 0.0429        | 80.0  | 80   | 2.2222          | 0.7273   |
| 0.0429        | 81.0  | 81   | 2.2803          | 0.7273   |
| 0.0429        | 82.0  | 82   | 2.3327          | 0.7273   |
| 0.0429        | 83.0  | 83   | 2.3643          | 0.7273   |
| 0.0429        | 84.0  | 84   | 2.3774          | 0.7273   |
| 0.0429        | 85.0  | 85   | 2.3838          | 0.7273   |
| 0.0429        | 86.0  | 86   | 2.4072          | 0.7273   |
| 0.0429        | 87.0  | 87   | 2.4189          | 0.7273   |
| 0.0429        | 88.0  | 88   | 2.4028          | 0.7273   |
| 0.0429        | 89.0  | 89   | 2.3847          | 0.7273   |
| 0.0475        | 90.0  | 90   | 2.3793          | 0.7273   |
| 0.0475        | 91.0  | 91   | 2.3831          | 0.7273   |
| 0.0475        | 92.0  | 92   | 2.3882          | 0.7273   |
| 0.0475        | 93.0  | 93   | 2.3964          | 0.7273   |
| 0.0475        | 94.0  | 94   | 2.4126          | 0.7273   |
| 0.0475        | 95.0  | 95   | 2.4309          | 0.7273   |
| 0.0475        | 96.0  | 96   | 2.4486          | 0.7273   |
| 0.0475        | 97.0  | 97   | 2.4628          | 0.7273   |
| 0.0475        | 98.0  | 98   | 2.4723          | 0.7273   |
| 0.0475        | 99.0  | 99   | 2.4775          | 0.7273   |
| 0.0337        | 100.0 | 100  | 2.4788          | 0.7273   |


### Framework versions

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

hafi̇f orta şi̇ddetli̇