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# deit-base-distilled-patch16-224-hasta-75-fold2

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: 0.5330
- 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    | 0.8721          | 0.5833   |
| No log        | 2.0   | 2    | 0.7324          | 0.8333   |
| No log        | 3.0   | 3    | 0.5330          | 0.9167   |
| No log        | 4.0   | 4    | 0.4022          | 0.9167   |
| No log        | 5.0   | 5    | 0.3794          | 0.9167   |
| No log        | 6.0   | 6    | 0.3910          | 0.9167   |
| No log        | 7.0   | 7    | 0.3832          | 0.9167   |
| No log        | 8.0   | 8    | 0.3524          | 0.9167   |
| No log        | 9.0   | 9    | 0.4280          | 0.9167   |
| 0.3237        | 10.0  | 10   | 0.5286          | 0.8333   |
| 0.3237        | 11.0  | 11   | 0.4004          | 0.9167   |
| 0.3237        | 12.0  | 12   | 0.3327          | 0.9167   |
| 0.3237        | 13.0  | 13   | 0.3136          | 0.9167   |
| 0.3237        | 14.0  | 14   | 0.2844          | 0.9167   |
| 0.3237        | 15.0  | 15   | 0.2493          | 0.9167   |
| 0.3237        | 16.0  | 16   | 0.2190          | 0.9167   |
| 0.3237        | 17.0  | 17   | 0.1925          | 0.9167   |
| 0.3237        | 18.0  | 18   | 0.1722          | 0.9167   |
| 0.3237        | 19.0  | 19   | 0.1387          | 0.9167   |
| 0.142         | 20.0  | 20   | 0.1259          | 0.9167   |
| 0.142         | 21.0  | 21   | 0.1443          | 0.9167   |
| 0.142         | 22.0  | 22   | 0.1372          | 0.9167   |
| 0.142         | 23.0  | 23   | 0.1043          | 0.9167   |
| 0.142         | 24.0  | 24   | 0.1022          | 0.9167   |
| 0.142         | 25.0  | 25   | 0.1327          | 0.9167   |
| 0.142         | 26.0  | 26   | 0.2213          | 0.9167   |
| 0.142         | 27.0  | 27   | 0.2587          | 0.9167   |
| 0.142         | 28.0  | 28   | 0.2411          | 0.9167   |
| 0.142         | 29.0  | 29   | 0.1915          | 0.9167   |
| 0.0723        | 30.0  | 30   | 0.1418          | 0.9167   |
| 0.0723        | 31.0  | 31   | 0.1369          | 0.9167   |
| 0.0723        | 32.0  | 32   | 0.1749          | 0.9167   |
| 0.0723        | 33.0  | 33   | 0.2607          | 0.9167   |
| 0.0723        | 34.0  | 34   | 0.3049          | 0.9167   |
| 0.0723        | 35.0  | 35   | 0.3103          | 0.9167   |
| 0.0723        | 36.0  | 36   | 0.2972          | 0.9167   |
| 0.0723        | 37.0  | 37   | 0.2901          | 0.9167   |
| 0.0723        | 38.0  | 38   | 0.2490          | 0.9167   |
| 0.0723        | 39.0  | 39   | 0.2047          | 0.9167   |
| 0.0458        | 40.0  | 40   | 0.1781          | 0.9167   |
| 0.0458        | 41.0  | 41   | 0.1712          | 0.9167   |
| 0.0458        | 42.0  | 42   | 0.2114          | 0.9167   |
| 0.0458        | 43.0  | 43   | 0.2837          | 0.9167   |
| 0.0458        | 44.0  | 44   | 0.3335          | 0.9167   |
| 0.0458        | 45.0  | 45   | 0.3600          | 0.9167   |
| 0.0458        | 46.0  | 46   | 0.3698          | 0.9167   |
| 0.0458        | 47.0  | 47   | 0.3607          | 0.9167   |
| 0.0458        | 48.0  | 48   | 0.3493          | 0.9167   |
| 0.0458        | 49.0  | 49   | 0.3408          | 0.9167   |
| 0.0478        | 50.0  | 50   | 0.3538          | 0.9167   |
| 0.0478        | 51.0  | 51   | 0.3481          | 0.9167   |
| 0.0478        | 52.0  | 52   | 0.3513          | 0.9167   |
| 0.0478        | 53.0  | 53   | 0.3336          | 0.9167   |
| 0.0478        | 54.0  | 54   | 0.3044          | 0.9167   |
| 0.0478        | 55.0  | 55   | 0.2844          | 0.9167   |
| 0.0478        | 56.0  | 56   | 0.2790          | 0.9167   |
| 0.0478        | 57.0  | 57   | 0.2990          | 0.9167   |
| 0.0478        | 58.0  | 58   | 0.3265          | 0.9167   |
| 0.0478        | 59.0  | 59   | 0.3682          | 0.9167   |
| 0.0145        | 60.0  | 60   | 0.3938          | 0.9167   |
| 0.0145        | 61.0  | 61   | 0.4018          | 0.9167   |
| 0.0145        | 62.0  | 62   | 0.3817          | 0.9167   |
| 0.0145        | 63.0  | 63   | 0.3376          | 0.9167   |
| 0.0145        | 64.0  | 64   | 0.2812          | 0.9167   |
| 0.0145        | 65.0  | 65   | 0.2029          | 0.9167   |
| 0.0145        | 66.0  | 66   | 0.1343          | 0.9167   |
| 0.0145        | 67.0  | 67   | 0.0996          | 0.9167   |
| 0.0145        | 68.0  | 68   | 0.0811          | 0.9167   |
| 0.0145        | 69.0  | 69   | 0.0662          | 0.9167   |
| 0.0447        | 70.0  | 70   | 0.0745          | 0.9167   |
| 0.0447        | 71.0  | 71   | 0.1053          | 0.9167   |
| 0.0447        | 72.0  | 72   | 0.1643          | 0.9167   |
| 0.0447        | 73.0  | 73   | 0.2353          | 0.9167   |
| 0.0447        | 74.0  | 74   | 0.3155          | 0.9167   |
| 0.0447        | 75.0  | 75   | 0.3678          | 0.9167   |
| 0.0447        | 76.0  | 76   | 0.3946          | 0.9167   |
| 0.0447        | 77.0  | 77   | 0.4025          | 0.9167   |
| 0.0447        | 78.0  | 78   | 0.4106          | 0.9167   |
| 0.0447        | 79.0  | 79   | 0.4147          | 0.9167   |
| 0.0229        | 80.0  | 80   | 0.4108          | 0.9167   |
| 0.0229        | 81.0  | 81   | 0.3993          | 0.9167   |
| 0.0229        | 82.0  | 82   | 0.3857          | 0.9167   |
| 0.0229        | 83.0  | 83   | 0.3644          | 0.9167   |
| 0.0229        | 84.0  | 84   | 0.3422          | 0.9167   |
| 0.0229        | 85.0  | 85   | 0.3280          | 0.9167   |
| 0.0229        | 86.0  | 86   | 0.3108          | 0.9167   |
| 0.0229        | 87.0  | 87   | 0.2936          | 0.9167   |
| 0.0229        | 88.0  | 88   | 0.2846          | 0.9167   |
| 0.0229        | 89.0  | 89   | 0.2861          | 0.9167   |
| 0.0317        | 90.0  | 90   | 0.2909          | 0.9167   |
| 0.0317        | 91.0  | 91   | 0.2921          | 0.9167   |
| 0.0317        | 92.0  | 92   | 0.2938          | 0.9167   |
| 0.0317        | 93.0  | 93   | 0.2974          | 0.9167   |
| 0.0317        | 94.0  | 94   | 0.2998          | 0.9167   |
| 0.0317        | 95.0  | 95   | 0.2994          | 0.9167   |
| 0.0317        | 96.0  | 96   | 0.2992          | 0.9167   |
| 0.0317        | 97.0  | 97   | 0.2973          | 0.9167   |
| 0.0317        | 98.0  | 98   | 0.2969          | 0.9167   |
| 0.0317        | 99.0  | 99   | 0.2970          | 0.9167   |
| 0.0283        | 100.0 | 100  | 0.2973          | 0.9167   |


### Framework versions

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

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