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# beit-base-patch16-224-hasta-85-fold1

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: 0.9074
- 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    | 1.4041          | 0.2727   |
| No log        | 2.0   | 2    | 1.1167          | 0.5455   |
| No log        | 3.0   | 3    | 0.7944          | 0.6364   |
| No log        | 4.0   | 4    | 0.9074          | 0.7273   |
| No log        | 5.0   | 5    | 1.3600          | 0.7273   |
| No log        | 6.0   | 6    | 1.6643          | 0.7273   |
| No log        | 7.0   | 7    | 1.6410          | 0.7273   |
| No log        | 8.0   | 8    | 1.4549          | 0.7273   |
| No log        | 9.0   | 9    | 1.4814          | 0.3636   |
| 0.4133        | 10.0  | 10   | 1.2719          | 0.6364   |
| 0.4133        | 11.0  | 11   | 1.0360          | 0.7273   |
| 0.4133        | 12.0  | 12   | 1.0691          | 0.7273   |
| 0.4133        | 13.0  | 13   | 0.9528          | 0.7273   |
| 0.4133        | 14.0  | 14   | 1.0430          | 0.7273   |
| 0.4133        | 15.0  | 15   | 1.3221          | 0.7273   |
| 0.4133        | 16.0  | 16   | 1.5236          | 0.7273   |
| 0.4133        | 17.0  | 17   | 1.5914          | 0.7273   |
| 0.4133        | 18.0  | 18   | 1.5015          | 0.7273   |
| 0.4133        | 19.0  | 19   | 1.3859          | 0.7273   |
| 0.1601        | 20.0  | 20   | 1.4514          | 0.7273   |
| 0.1601        | 21.0  | 21   | 1.6169          | 0.7273   |
| 0.1601        | 22.0  | 22   | 1.5223          | 0.7273   |
| 0.1601        | 23.0  | 23   | 1.4172          | 0.7273   |
| 0.1601        | 24.0  | 24   | 1.4753          | 0.6364   |
| 0.1601        | 25.0  | 25   | 1.5207          | 0.7273   |
| 0.1601        | 26.0  | 26   | 1.7055          | 0.7273   |
| 0.1601        | 27.0  | 27   | 1.7901          | 0.7273   |
| 0.1601        | 28.0  | 28   | 1.9109          | 0.7273   |
| 0.1601        | 29.0  | 29   | 1.8261          | 0.7273   |
| 0.1015        | 30.0  | 30   | 1.5657          | 0.7273   |
| 0.1015        | 31.0  | 31   | 1.3513          | 0.7273   |
| 0.1015        | 32.0  | 32   | 1.3374          | 0.7273   |
| 0.1015        | 33.0  | 33   | 1.4942          | 0.7273   |
| 0.1015        | 34.0  | 34   | 1.7731          | 0.7273   |
| 0.1015        | 35.0  | 35   | 1.8369          | 0.7273   |
| 0.1015        | 36.0  | 36   | 1.7328          | 0.7273   |
| 0.1015        | 37.0  | 37   | 1.5451          | 0.7273   |
| 0.1015        | 38.0  | 38   | 1.4483          | 0.7273   |
| 0.1015        | 39.0  | 39   | 1.4289          | 0.7273   |
| 0.0819        | 40.0  | 40   | 1.3922          | 0.7273   |
| 0.0819        | 41.0  | 41   | 1.4002          | 0.7273   |
| 0.0819        | 42.0  | 42   | 1.3673          | 0.7273   |
| 0.0819        | 43.0  | 43   | 1.2194          | 0.7273   |
| 0.0819        | 44.0  | 44   | 1.2879          | 0.7273   |
| 0.0819        | 45.0  | 45   | 1.4545          | 0.7273   |
| 0.0819        | 46.0  | 46   | 1.6902          | 0.7273   |
| 0.0819        | 47.0  | 47   | 1.8415          | 0.7273   |
| 0.0819        | 48.0  | 48   | 1.8891          | 0.7273   |
| 0.0819        | 49.0  | 49   | 1.8339          | 0.7273   |
| 0.0551        | 50.0  | 50   | 1.8433          | 0.7273   |
| 0.0551        | 51.0  | 51   | 1.8432          | 0.7273   |
| 0.0551        | 52.0  | 52   | 1.8002          | 0.7273   |
| 0.0551        | 53.0  | 53   | 1.7276          | 0.7273   |
| 0.0551        | 54.0  | 54   | 1.5764          | 0.7273   |
| 0.0551        | 55.0  | 55   | 1.3951          | 0.7273   |
| 0.0551        | 56.0  | 56   | 1.4243          | 0.7273   |
| 0.0551        | 57.0  | 57   | 1.5620          | 0.7273   |
| 0.0551        | 58.0  | 58   | 1.6897          | 0.7273   |
| 0.0551        | 59.0  | 59   | 1.7995          | 0.7273   |
| 0.0413        | 60.0  | 60   | 1.8739          | 0.7273   |
| 0.0413        | 61.0  | 61   | 1.8486          | 0.7273   |
| 0.0413        | 62.0  | 62   | 1.8073          | 0.7273   |
| 0.0413        | 63.0  | 63   | 1.8193          | 0.7273   |
| 0.0413        | 64.0  | 64   | 1.7604          | 0.7273   |
| 0.0413        | 65.0  | 65   | 1.6327          | 0.7273   |
| 0.0413        | 66.0  | 66   | 1.5447          | 0.7273   |
| 0.0413        | 67.0  | 67   | 1.4243          | 0.7273   |
| 0.0413        | 68.0  | 68   | 1.3810          | 0.7273   |
| 0.0413        | 69.0  | 69   | 1.3641          | 0.7273   |
| 0.038         | 70.0  | 70   | 1.4688          | 0.7273   |
| 0.038         | 71.0  | 71   | 1.5677          | 0.7273   |
| 0.038         | 72.0  | 72   | 1.7174          | 0.7273   |
| 0.038         | 73.0  | 73   | 1.7920          | 0.7273   |
| 0.038         | 74.0  | 74   | 1.9000          | 0.7273   |
| 0.038         | 75.0  | 75   | 1.9468          | 0.7273   |
| 0.038         | 76.0  | 76   | 1.9872          | 0.7273   |
| 0.038         | 77.0  | 77   | 2.0208          | 0.7273   |
| 0.038         | 78.0  | 78   | 2.0135          | 0.7273   |
| 0.038         | 79.0  | 79   | 1.9762          | 0.7273   |
| 0.0365        | 80.0  | 80   | 1.9576          | 0.7273   |
| 0.0365        | 81.0  | 81   | 1.9310          | 0.7273   |
| 0.0365        | 82.0  | 82   | 1.8495          | 0.7273   |
| 0.0365        | 83.0  | 83   | 1.7683          | 0.7273   |
| 0.0365        | 84.0  | 84   | 1.7109          | 0.7273   |
| 0.0365        | 85.0  | 85   | 1.6438          | 0.7273   |
| 0.0365        | 86.0  | 86   | 1.6154          | 0.7273   |
| 0.0365        | 87.0  | 87   | 1.5715          | 0.7273   |
| 0.0365        | 88.0  | 88   | 1.5428          | 0.7273   |
| 0.0365        | 89.0  | 89   | 1.5164          | 0.7273   |
| 0.038         | 90.0  | 90   | 1.5008          | 0.7273   |
| 0.038         | 91.0  | 91   | 1.4730          | 0.7273   |
| 0.038         | 92.0  | 92   | 1.4493          | 0.7273   |
| 0.038         | 93.0  | 93   | 1.4728          | 0.7273   |
| 0.038         | 94.0  | 94   | 1.5033          | 0.7273   |
| 0.038         | 95.0  | 95   | 1.5346          | 0.7273   |
| 0.038         | 96.0  | 96   | 1.5556          | 0.7273   |
| 0.038         | 97.0  | 97   | 1.5643          | 0.7273   |
| 0.038         | 98.0  | 98   | 1.5759          | 0.7273   |
| 0.038         | 99.0  | 99   | 1.5807          | 0.7273   |
| 0.0191        | 100.0 | 100  | 1.5806          | 0.7273   |


### Framework versions

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

hafi̇f orta şi̇ddetli̇