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# beit-base-patch16-224-hasta-65-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: 0.6728
- Accuracy: 0.75

## 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        | 0.5714  | 1    | 1.3013          | 0.3056   |
| No log        | 1.7143  | 3    | 1.2799          | 0.2778   |
| No log        | 2.8571  | 5    | 1.2588          | 0.3333   |
| No log        | 4.0     | 7    | 1.1296          | 0.3889   |
| No log        | 4.5714  | 8    | 1.1295          | 0.3611   |
| 1.1611        | 5.7143  | 10   | 1.2689          | 0.25     |
| 1.1611        | 6.8571  | 12   | 1.0895          | 0.3889   |
| 1.1611        | 8.0     | 14   | 1.0978          | 0.5      |
| 1.1611        | 8.5714  | 15   | 1.1168          | 0.5278   |
| 1.1611        | 9.7143  | 17   | 1.0844          | 0.4167   |
| 1.1611        | 10.8571 | 19   | 1.0476          | 0.5      |
| 0.9913        | 12.0    | 21   | 1.2315          | 0.4722   |
| 0.9913        | 12.5714 | 22   | 1.1444          | 0.4722   |
| 0.9913        | 13.7143 | 24   | 1.0242          | 0.5      |
| 0.9913        | 14.8571 | 26   | 1.0495          | 0.5278   |
| 0.9913        | 16.0    | 28   | 1.1234          | 0.4722   |
| 0.9913        | 16.5714 | 29   | 1.2332          | 0.5278   |
| 0.9206        | 17.7143 | 31   | 1.4389          | 0.3611   |
| 0.9206        | 18.8571 | 33   | 1.0300          | 0.5      |
| 0.9206        | 20.0    | 35   | 1.0028          | 0.5278   |
| 0.9206        | 20.5714 | 36   | 1.0322          | 0.5      |
| 0.9206        | 21.7143 | 38   | 1.0871          | 0.5278   |
| 0.7309        | 22.8571 | 40   | 0.9616          | 0.4722   |
| 0.7309        | 24.0    | 42   | 0.9571          | 0.5556   |
| 0.7309        | 24.5714 | 43   | 0.9855          | 0.5278   |
| 0.7309        | 25.7143 | 45   | 0.9598          | 0.5278   |
| 0.7309        | 26.8571 | 47   | 0.9774          | 0.5278   |
| 0.7309        | 28.0    | 49   | 0.9205          | 0.5556   |
| 0.6039        | 28.5714 | 50   | 0.9073          | 0.5556   |
| 0.6039        | 29.7143 | 52   | 0.8644          | 0.5833   |
| 0.6039        | 30.8571 | 54   | 0.8931          | 0.5833   |
| 0.6039        | 32.0    | 56   | 0.8686          | 0.6111   |
| 0.6039        | 32.5714 | 57   | 0.8381          | 0.5833   |
| 0.6039        | 33.7143 | 59   | 0.8658          | 0.5556   |
| 0.4784        | 34.8571 | 61   | 0.9915          | 0.5556   |
| 0.4784        | 36.0    | 63   | 0.7971          | 0.5833   |
| 0.4784        | 36.5714 | 64   | 0.7682          | 0.6111   |
| 0.4784        | 37.7143 | 66   | 0.9361          | 0.5833   |
| 0.4784        | 38.8571 | 68   | 0.9093          | 0.5833   |
| 0.4469        | 40.0    | 70   | 0.6728          | 0.75     |
| 0.4469        | 40.5714 | 71   | 0.6415          | 0.7222   |
| 0.4469        | 41.7143 | 73   | 0.7045          | 0.6667   |
| 0.4469        | 42.8571 | 75   | 0.8974          | 0.6389   |
| 0.4469        | 44.0    | 77   | 0.8032          | 0.6111   |
| 0.4469        | 44.5714 | 78   | 0.7134          | 0.6944   |
| 0.4329        | 45.7143 | 80   | 0.6975          | 0.7222   |
| 0.4329        | 46.8571 | 82   | 0.6758          | 0.7222   |
| 0.4329        | 48.0    | 84   | 0.8327          | 0.6111   |
| 0.4329        | 48.5714 | 85   | 0.9089          | 0.6111   |
| 0.4329        | 49.7143 | 87   | 0.9158          | 0.6111   |
| 0.4329        | 50.8571 | 89   | 0.8007          | 0.6389   |
| 0.4282        | 52.0    | 91   | 0.7363          | 0.6389   |
| 0.4282        | 52.5714 | 92   | 0.7378          | 0.6389   |
| 0.4282        | 53.7143 | 94   | 0.7449          | 0.6111   |
| 0.4282        | 54.8571 | 96   | 0.7605          | 0.6111   |
| 0.4282        | 56.0    | 98   | 0.7853          | 0.6111   |
| 0.4282        | 56.5714 | 99   | 0.7903          | 0.5833   |
| 0.3188        | 57.1429 | 100  | 0.7926          | 0.5833   |


### Framework versions

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

hafi̇f orta şi̇ddetli̇