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# deit-base-distilled-patch16-224-hasta-75-fold1

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: 0.1907
- 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    | 1.0839          | 0.4167   |
| No log        | 2.0   | 2    | 0.8911          | 0.6667   |
| No log        | 3.0   | 3    | 0.5837          | 0.75     |
| No log        | 4.0   | 4    | 0.3481          | 0.9167   |
| No log        | 5.0   | 5    | 0.2815          | 0.9167   |
| No log        | 6.0   | 6    | 0.2839          | 0.9167   |
| No log        | 7.0   | 7    | 0.2838          | 0.9167   |
| No log        | 8.0   | 8    | 0.2692          | 0.9167   |
| No log        | 9.0   | 9    | 0.2701          | 0.9167   |
| 0.3107        | 10.0  | 10   | 0.3363          | 0.9167   |
| 0.3107        | 11.0  | 11   | 0.3816          | 0.9167   |
| 0.3107        | 12.0  | 12   | 0.3427          | 0.9167   |
| 0.3107        | 13.0  | 13   | 0.2728          | 0.9167   |
| 0.3107        | 14.0  | 14   | 0.2273          | 0.9167   |
| 0.3107        | 15.0  | 15   | 0.2052          | 0.9167   |
| 0.3107        | 16.0  | 16   | 0.1840          | 0.9167   |
| 0.3107        | 17.0  | 17   | 0.1907          | 1.0      |
| 0.3107        | 18.0  | 18   | 0.1761          | 1.0      |
| 0.3107        | 19.0  | 19   | 0.1302          | 1.0      |
| 0.1503        | 20.0  | 20   | 0.0937          | 1.0      |
| 0.1503        | 21.0  | 21   | 0.0767          | 1.0      |
| 0.1503        | 22.0  | 22   | 0.0642          | 1.0      |
| 0.1503        | 23.0  | 23   | 0.0730          | 1.0      |
| 0.1503        | 24.0  | 24   | 0.0937          | 1.0      |
| 0.1503        | 25.0  | 25   | 0.0713          | 1.0      |
| 0.1503        | 26.0  | 26   | 0.0476          | 1.0      |
| 0.1503        | 27.0  | 27   | 0.0462          | 1.0      |
| 0.1503        | 28.0  | 28   | 0.0553          | 1.0      |
| 0.1503        | 29.0  | 29   | 0.0689          | 1.0      |
| 0.068         | 30.0  | 30   | 0.0676          | 1.0      |
| 0.068         | 31.0  | 31   | 0.0534          | 1.0      |
| 0.068         | 32.0  | 32   | 0.0472          | 1.0      |
| 0.068         | 33.0  | 33   | 0.0575          | 1.0      |
| 0.068         | 34.0  | 34   | 0.0614          | 1.0      |
| 0.068         | 35.0  | 35   | 0.0596          | 1.0      |
| 0.068         | 36.0  | 36   | 0.0503          | 1.0      |
| 0.068         | 37.0  | 37   | 0.0571          | 1.0      |
| 0.068         | 38.0  | 38   | 0.0694          | 1.0      |
| 0.068         | 39.0  | 39   | 0.0869          | 0.9167   |
| 0.0416        | 40.0  | 40   | 0.1039          | 0.9167   |
| 0.0416        | 41.0  | 41   | 0.1130          | 0.9167   |
| 0.0416        | 42.0  | 42   | 0.1127          | 0.9167   |
| 0.0416        | 43.0  | 43   | 0.1028          | 0.9167   |
| 0.0416        | 44.0  | 44   | 0.0840          | 0.9167   |
| 0.0416        | 45.0  | 45   | 0.0703          | 0.9167   |
| 0.0416        | 46.0  | 46   | 0.0564          | 1.0      |
| 0.0416        | 47.0  | 47   | 0.0572          | 1.0      |
| 0.0416        | 48.0  | 48   | 0.0689          | 0.9167   |
| 0.0416        | 49.0  | 49   | 0.0967          | 0.9167   |
| 0.048         | 50.0  | 50   | 0.1440          | 0.9167   |
| 0.048         | 51.0  | 51   | 0.1667          | 0.9167   |
| 0.048         | 52.0  | 52   | 0.1823          | 0.9167   |
| 0.048         | 53.0  | 53   | 0.1734          | 0.9167   |
| 0.048         | 54.0  | 54   | 0.1543          | 0.9167   |
| 0.048         | 55.0  | 55   | 0.1383          | 0.9167   |
| 0.048         | 56.0  | 56   | 0.1266          | 0.9167   |
| 0.048         | 57.0  | 57   | 0.1013          | 0.9167   |
| 0.048         | 58.0  | 58   | 0.0830          | 0.9167   |
| 0.048         | 59.0  | 59   | 0.0780          | 0.9167   |
| 0.0118        | 60.0  | 60   | 0.0756          | 0.9167   |
| 0.0118        | 61.0  | 61   | 0.0723          | 0.9167   |
| 0.0118        | 62.0  | 62   | 0.0563          | 1.0      |
| 0.0118        | 63.0  | 63   | 0.0470          | 1.0      |
| 0.0118        | 64.0  | 64   | 0.0469          | 1.0      |
| 0.0118        | 65.0  | 65   | 0.0522          | 1.0      |
| 0.0118        | 66.0  | 66   | 0.0576          | 1.0      |
| 0.0118        | 67.0  | 67   | 0.0639          | 1.0      |
| 0.0118        | 68.0  | 68   | 0.0827          | 0.9167   |
| 0.0118        | 69.0  | 69   | 0.1089          | 0.9167   |
| 0.0271        | 70.0  | 70   | 0.1343          | 0.9167   |
| 0.0271        | 71.0  | 71   | 0.1514          | 0.9167   |
| 0.0271        | 72.0  | 72   | 0.1552          | 0.9167   |
| 0.0271        | 73.0  | 73   | 0.1500          | 0.9167   |
| 0.0271        | 74.0  | 74   | 0.1392          | 0.9167   |
| 0.0271        | 75.0  | 75   | 0.1229          | 0.9167   |
| 0.0271        | 76.0  | 76   | 0.1009          | 0.9167   |
| 0.0271        | 77.0  | 77   | 0.0858          | 0.9167   |
| 0.0271        | 78.0  | 78   | 0.0844          | 0.9167   |
| 0.0271        | 79.0  | 79   | 0.0855          | 0.9167   |
| 0.0462        | 80.0  | 80   | 0.0972          | 0.9167   |
| 0.0462        | 81.0  | 81   | 0.1140          | 0.9167   |
| 0.0462        | 82.0  | 82   | 0.1398          | 0.9167   |
| 0.0462        | 83.0  | 83   | 0.1639          | 0.9167   |
| 0.0462        | 84.0  | 84   | 0.1842          | 0.9167   |
| 0.0462        | 85.0  | 85   | 0.1938          | 0.9167   |
| 0.0462        | 86.0  | 86   | 0.2000          | 0.9167   |
| 0.0462        | 87.0  | 87   | 0.2008          | 0.9167   |
| 0.0462        | 88.0  | 88   | 0.1949          | 0.9167   |
| 0.0462        | 89.0  | 89   | 0.1896          | 0.9167   |
| 0.022         | 90.0  | 90   | 0.1798          | 0.9167   |
| 0.022         | 91.0  | 91   | 0.1700          | 0.9167   |
| 0.022         | 92.0  | 92   | 0.1617          | 0.9167   |
| 0.022         | 93.0  | 93   | 0.1549          | 0.9167   |
| 0.022         | 94.0  | 94   | 0.1492          | 0.9167   |
| 0.022         | 95.0  | 95   | 0.1447          | 0.9167   |
| 0.022         | 96.0  | 96   | 0.1435          | 0.9167   |
| 0.022         | 97.0  | 97   | 0.1431          | 0.9167   |
| 0.022         | 98.0  | 98   | 0.1418          | 0.9167   |
| 0.022         | 99.0  | 99   | 0.1411          | 0.9167   |
| 0.0236        | 100.0 | 100  | 0.1408          | 0.9167   |


### Framework versions

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

hafi̇f orta şi̇ddetli̇