
<!-- 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. -->

# deit-base-distilled-patch16-224-hasta-75-fold3

This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5699
- Accuracy: 0.9167

## 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    | 1.0629          | 0.1667   |
| No log        | 2.0   | 2    | 0.8596          | 0.5833   |
| No log        | 3.0   | 3    | 0.5699          | 0.9167   |
| No log        | 4.0   | 4    | 0.3576          | 0.9167   |
| No log        | 5.0   | 5    | 0.2875          | 0.9167   |
| No log        | 6.0   | 6    | 0.2993          | 0.9167   |
| No log        | 7.0   | 7    | 0.3152          | 0.9167   |
| No log        | 8.0   | 8    | 0.3399          | 0.9167   |
| No log        | 9.0   | 9    | 0.4232          | 0.9167   |
| 0.3622        | 10.0  | 10   | 0.5129          | 0.9167   |
| 0.3622        | 11.0  | 11   | 0.4640          | 0.9167   |
| 0.3622        | 12.0  | 12   | 0.3709          | 0.9167   |
| 0.3622        | 13.0  | 13   | 0.3229          | 0.9167   |
| 0.3622        | 14.0  | 14   | 0.3183          | 0.9167   |
| 0.3622        | 15.0  | 15   | 0.3590          | 0.9167   |
| 0.3622        | 16.0  | 16   | 0.4468          | 0.9167   |
| 0.3622        | 17.0  | 17   | 0.5249          | 0.75     |
| 0.3622        | 18.0  | 18   | 0.4227          | 0.9167   |
| 0.3622        | 19.0  | 19   | 0.3300          | 0.9167   |
| 0.1749        | 20.0  | 20   | 0.3031          | 0.9167   |
| 0.1749        | 21.0  | 21   | 0.2836          | 0.9167   |
| 0.1749        | 22.0  | 22   | 0.2596          | 0.9167   |
| 0.1749        | 23.0  | 23   | 0.2215          | 0.9167   |
| 0.1749        | 24.0  | 24   | 0.1797          | 0.9167   |
| 0.1749        | 25.0  | 25   | 0.1658          | 0.9167   |
| 0.1749        | 26.0  | 26   | 0.1600          | 0.9167   |
| 0.1749        | 27.0  | 27   | 0.1567          | 0.9167   |
| 0.1749        | 28.0  | 28   | 0.1680          | 0.9167   |
| 0.1749        | 29.0  | 29   | 0.2079          | 0.9167   |
| 0.1002        | 30.0  | 30   | 0.2458          | 0.9167   |
| 0.1002        | 31.0  | 31   | 0.2637          | 0.9167   |
| 0.1002        | 32.0  | 32   | 0.2795          | 0.9167   |
| 0.1002        | 33.0  | 33   | 0.3174          | 0.9167   |
| 0.1002        | 34.0  | 34   | 0.3426          | 0.9167   |
| 0.1002        | 35.0  | 35   | 0.3858          | 0.9167   |
| 0.1002        | 36.0  | 36   | 0.4281          | 0.9167   |
| 0.1002        | 37.0  | 37   | 0.4447          | 0.9167   |
| 0.1002        | 38.0  | 38   | 0.4355          | 0.9167   |
| 0.1002        | 39.0  | 39   | 0.4306          | 0.9167   |
| 0.0662        | 40.0  | 40   | 0.4398          | 0.9167   |
| 0.0662        | 41.0  | 41   | 0.4657          | 0.9167   |
| 0.0662        | 42.0  | 42   | 0.4941          | 0.9167   |
| 0.0662        | 43.0  | 43   | 0.5061          | 0.9167   |
| 0.0662        | 44.0  | 44   | 0.5010          | 0.9167   |
| 0.0662        | 45.0  | 45   | 0.4816          | 0.9167   |
| 0.0662        | 46.0  | 46   | 0.4398          | 0.9167   |
| 0.0662        | 47.0  | 47   | 0.3948          | 0.9167   |
| 0.0662        | 48.0  | 48   | 0.3486          | 0.9167   |
| 0.0662        | 49.0  | 49   | 0.3180          | 0.9167   |
| 0.0543        | 50.0  | 50   | 0.3029          | 0.9167   |
| 0.0543        | 51.0  | 51   | 0.3053          | 0.9167   |
| 0.0543        | 52.0  | 52   | 0.3160          | 0.9167   |
| 0.0543        | 53.0  | 53   | 0.3449          | 0.9167   |
| 0.0543        | 54.0  | 54   | 0.3712          | 0.9167   |
| 0.0543        | 55.0  | 55   | 0.3785          | 0.9167   |
| 0.0543        | 56.0  | 56   | 0.4049          | 0.9167   |
| 0.0543        | 57.0  | 57   | 0.4094          | 0.9167   |
| 0.0543        | 58.0  | 58   | 0.4179          | 0.9167   |
| 0.0543        | 59.0  | 59   | 0.4083          | 0.9167   |
| 0.0473        | 60.0  | 60   | 0.3855          | 0.9167   |
| 0.0473        | 61.0  | 61   | 0.3758          | 0.9167   |
| 0.0473        | 62.0  | 62   | 0.3675          | 0.9167   |
| 0.0473        | 63.0  | 63   | 0.3660          | 0.9167   |
| 0.0473        | 64.0  | 64   | 0.3843          | 0.9167   |
| 0.0473        | 65.0  | 65   | 0.4092          | 0.9167   |
| 0.0473        | 66.0  | 66   | 0.4374          | 0.9167   |
| 0.0473        | 67.0  | 67   | 0.4666          | 0.9167   |
| 0.0473        | 68.0  | 68   | 0.4798          | 0.9167   |
| 0.0473        | 69.0  | 69   | 0.4869          | 0.9167   |
| 0.018         | 70.0  | 70   | 0.4853          | 0.9167   |
| 0.018         | 71.0  | 71   | 0.4783          | 0.9167   |
| 0.018         | 72.0  | 72   | 0.4649          | 0.9167   |
| 0.018         | 73.0  | 73   | 0.4525          | 0.9167   |
| 0.018         | 74.0  | 74   | 0.4411          | 0.9167   |
| 0.018         | 75.0  | 75   | 0.4355          | 0.9167   |
| 0.018         | 76.0  | 76   | 0.4310          | 0.9167   |
| 0.018         | 77.0  | 77   | 0.4330          | 0.9167   |
| 0.018         | 78.0  | 78   | 0.4296          | 0.9167   |
| 0.018         | 79.0  | 79   | 0.4264          | 0.9167   |
| 0.0195        | 80.0  | 80   | 0.4204          | 0.9167   |
| 0.0195        | 81.0  | 81   | 0.4182          | 0.9167   |
| 0.0195        | 82.0  | 82   | 0.4193          | 0.9167   |
| 0.0195        | 83.0  | 83   | 0.4225          | 0.9167   |
| 0.0195        | 84.0  | 84   | 0.4208          | 0.9167   |
| 0.0195        | 85.0  | 85   | 0.4193          | 0.9167   |
| 0.0195        | 86.0  | 86   | 0.4185          | 0.9167   |
| 0.0195        | 87.0  | 87   | 0.4186          | 0.9167   |
| 0.0195        | 88.0  | 88   | 0.4163          | 0.9167   |
| 0.0195        | 89.0  | 89   | 0.4076          | 0.9167   |
| 0.0203        | 90.0  | 90   | 0.4032          | 0.9167   |
| 0.0203        | 91.0  | 91   | 0.4000          | 0.9167   |
| 0.0203        | 92.0  | 92   | 0.4003          | 0.9167   |
| 0.0203        | 93.0  | 93   | 0.4033          | 0.9167   |
| 0.0203        | 94.0  | 94   | 0.4079          | 0.9167   |
| 0.0203        | 95.0  | 95   | 0.4112          | 0.9167   |
| 0.0203        | 96.0  | 96   | 0.4138          | 0.9167   |
| 0.0203        | 97.0  | 97   | 0.4155          | 0.9167   |
| 0.0203        | 98.0  | 98   | 0.4167          | 0.9167   |
| 0.0203        | 99.0  | 99   | 0.4169          | 0.9167   |
| 0.0252        | 100.0 | 100  | 0.4170          | 0.9167   |


### Framework versions

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

hafi̇f orta şi̇ddetli̇