
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# beit-base-patch16-224-hasta-75-fold4

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: 0.0812
- 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    | 2.3241          | 0.0833   |
| No log        | 2.0   | 2    | 1.8900          | 0.0833   |
| No log        | 3.0   | 3    | 1.1748          | 0.3333   |
| No log        | 4.0   | 4    | 0.5751          | 0.9167   |
| No log        | 5.0   | 5    | 0.4224          | 0.9167   |
| No log        | 6.0   | 6    | 0.3931          | 0.9167   |
| No log        | 7.0   | 7    | 0.3635          | 0.9167   |
| No log        | 8.0   | 8    | 0.5637          | 0.8333   |
| No log        | 9.0   | 9    | 0.4256          | 0.9167   |
| 0.4996        | 10.0  | 10   | 0.2830          | 0.9167   |
| 0.4996        | 11.0  | 11   | 0.2743          | 0.9167   |
| 0.4996        | 12.0  | 12   | 0.4505          | 0.9167   |
| 0.4996        | 13.0  | 13   | 0.3552          | 0.9167   |
| 0.4996        | 14.0  | 14   | 0.2453          | 0.9167   |
| 0.4996        | 15.0  | 15   | 0.2528          | 0.9167   |
| 0.4996        | 16.0  | 16   | 0.2926          | 0.9167   |
| 0.4996        | 17.0  | 17   | 0.3253          | 0.8333   |
| 0.4996        | 18.0  | 18   | 0.3367          | 0.8333   |
| 0.4996        | 19.0  | 19   | 0.3681          | 0.8333   |
| 0.1796        | 20.0  | 20   | 0.2677          | 0.9167   |
| 0.1796        | 21.0  | 21   | 0.2704          | 0.9167   |
| 0.1796        | 22.0  | 22   | 0.3116          | 0.9167   |
| 0.1796        | 23.0  | 23   | 0.3650          | 0.9167   |
| 0.1796        | 24.0  | 24   | 0.2170          | 0.9167   |
| 0.1796        | 25.0  | 25   | 0.2114          | 0.9167   |
| 0.1796        | 26.0  | 26   | 0.1976          | 0.9167   |
| 0.1796        | 27.0  | 27   | 0.1619          | 0.9167   |
| 0.1796        | 28.0  | 28   | 0.1646          | 0.9167   |
| 0.1796        | 29.0  | 29   | 0.1432          | 0.9167   |
| 0.1179        | 30.0  | 30   | 0.0812          | 1.0      |
| 0.1179        | 31.0  | 31   | 0.1163          | 1.0      |
| 0.1179        | 32.0  | 32   | 0.0898          | 1.0      |
| 0.1179        | 33.0  | 33   | 0.1190          | 0.9167   |
| 0.1179        | 34.0  | 34   | 0.1464          | 0.9167   |
| 0.1179        | 35.0  | 35   | 0.1136          | 1.0      |
| 0.1179        | 36.0  | 36   | 0.2270          | 0.9167   |
| 0.1179        | 37.0  | 37   | 0.2265          | 0.9167   |
| 0.1179        | 38.0  | 38   | 0.0995          | 1.0      |
| 0.1179        | 39.0  | 39   | 0.0853          | 1.0      |
| 0.1084        | 40.0  | 40   | 0.0858          | 1.0      |
| 0.1084        | 41.0  | 41   | 0.0970          | 1.0      |
| 0.1084        | 42.0  | 42   | 0.0949          | 1.0      |
| 0.1084        | 43.0  | 43   | 0.0709          | 1.0      |
| 0.1084        | 44.0  | 44   | 0.0807          | 0.9167   |
| 0.1084        | 45.0  | 45   | 0.1052          | 0.9167   |
| 0.1084        | 46.0  | 46   | 0.0629          | 1.0      |
| 0.1084        | 47.0  | 47   | 0.0272          | 1.0      |
| 0.1084        | 48.0  | 48   | 0.0775          | 1.0      |
| 0.1084        | 49.0  | 49   | 0.1113          | 1.0      |
| 0.0591        | 50.0  | 50   | 0.1189          | 1.0      |
| 0.0591        | 51.0  | 51   | 0.0526          | 1.0      |
| 0.0591        | 52.0  | 52   | 0.0262          | 1.0      |
| 0.0591        | 53.0  | 53   | 0.1035          | 0.9167   |
| 0.0591        | 54.0  | 54   | 0.1508          | 0.9167   |
| 0.0591        | 55.0  | 55   | 0.1280          | 0.9167   |
| 0.0591        | 56.0  | 56   | 0.0652          | 0.9167   |
| 0.0591        | 57.0  | 57   | 0.0357          | 1.0      |
| 0.0591        | 58.0  | 58   | 0.0407          | 1.0      |
| 0.0591        | 59.0  | 59   | 0.0430          | 1.0      |
| 0.0637        | 60.0  | 60   | 0.0468          | 1.0      |
| 0.0637        | 61.0  | 61   | 0.0997          | 0.9167   |
| 0.0637        | 62.0  | 62   | 0.2200          | 0.9167   |
| 0.0637        | 63.0  | 63   | 0.2979          | 0.9167   |
| 0.0637        | 64.0  | 64   | 0.3167          | 0.9167   |
| 0.0637        | 65.0  | 65   | 0.2611          | 0.9167   |
| 0.0637        | 66.0  | 66   | 0.1697          | 0.9167   |
| 0.0637        | 67.0  | 67   | 0.0669          | 0.9167   |
| 0.0637        | 68.0  | 68   | 0.0313          | 1.0      |
| 0.0637        | 69.0  | 69   | 0.0255          | 1.0      |
| 0.0446        | 70.0  | 70   | 0.0243          | 1.0      |
| 0.0446        | 71.0  | 71   | 0.0188          | 1.0      |
| 0.0446        | 72.0  | 72   | 0.0210          | 1.0      |
| 0.0446        | 73.0  | 73   | 0.0261          | 1.0      |
| 0.0446        | 74.0  | 74   | 0.0378          | 1.0      |
| 0.0446        | 75.0  | 75   | 0.0492          | 1.0      |
| 0.0446        | 76.0  | 76   | 0.0679          | 0.9167   |
| 0.0446        | 77.0  | 77   | 0.0958          | 0.9167   |
| 0.0446        | 78.0  | 78   | 0.0803          | 0.9167   |
| 0.0446        | 79.0  | 79   | 0.0455          | 1.0      |
| 0.0489        | 80.0  | 80   | 0.0194          | 1.0      |
| 0.0489        | 81.0  | 81   | 0.0141          | 1.0      |
| 0.0489        | 82.0  | 82   | 0.0109          | 1.0      |
| 0.0489        | 83.0  | 83   | 0.0104          | 1.0      |
| 0.0489        | 84.0  | 84   | 0.0108          | 1.0      |
| 0.0489        | 85.0  | 85   | 0.0121          | 1.0      |
| 0.0489        | 86.0  | 86   | 0.0118          | 1.0      |
| 0.0489        | 87.0  | 87   | 0.0109          | 1.0      |
| 0.0489        | 88.0  | 88   | 0.0107          | 1.0      |
| 0.0489        | 89.0  | 89   | 0.0107          | 1.0      |
| 0.0322        | 90.0  | 90   | 0.0107          | 1.0      |
| 0.0322        | 91.0  | 91   | 0.0107          | 1.0      |
| 0.0322        | 92.0  | 92   | 0.0106          | 1.0      |
| 0.0322        | 93.0  | 93   | 0.0105          | 1.0      |
| 0.0322        | 94.0  | 94   | 0.0105          | 1.0      |
| 0.0322        | 95.0  | 95   | 0.0105          | 1.0      |
| 0.0322        | 96.0  | 96   | 0.0106          | 1.0      |
| 0.0322        | 97.0  | 97   | 0.0106          | 1.0      |
| 0.0322        | 98.0  | 98   | 0.0106          | 1.0      |
| 0.0322        | 99.0  | 99   | 0.0108          | 1.0      |
| 0.0405        | 100.0 | 100  | 0.0109          | 1.0      |


### Framework versions

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

hafi̇f orta şi̇ddetli̇