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# deit-base-distilled-patch16-224-75-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: 0.3098
- Accuracy: 0.9070

## 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   | 2    | 0.6477          | 0.6512   |
| No log        | 2.0   | 4    | 0.6528          | 0.6977   |
| No log        | 3.0   | 6    | 0.8096          | 0.6977   |
| No log        | 4.0   | 8    | 0.7679          | 0.6977   |
| 0.5994        | 5.0   | 10   | 0.5482          | 0.6977   |
| 0.5994        | 6.0   | 12   | 0.4984          | 0.7442   |
| 0.5994        | 7.0   | 14   | 0.6156          | 0.6977   |
| 0.5994        | 8.0   | 16   | 0.5307          | 0.7674   |
| 0.5994        | 9.0   | 18   | 0.4036          | 0.7674   |
| 0.3806        | 10.0  | 20   | 0.4241          | 0.7907   |
| 0.3806        | 11.0  | 22   | 0.4263          | 0.8140   |
| 0.3806        | 12.0  | 24   | 0.6778          | 0.7442   |
| 0.3806        | 13.0  | 26   | 0.5885          | 0.7674   |
| 0.3806        | 14.0  | 28   | 0.6048          | 0.7907   |
| 0.273         | 15.0  | 30   | 0.5110          | 0.8140   |
| 0.273         | 16.0  | 32   | 0.3793          | 0.7442   |
| 0.273         | 17.0  | 34   | 0.3635          | 0.7907   |
| 0.273         | 18.0  | 36   | 0.3863          | 0.8140   |
| 0.273         | 19.0  | 38   | 0.3788          | 0.8372   |
| 0.2388        | 20.0  | 40   | 0.3390          | 0.8140   |
| 0.2388        | 21.0  | 42   | 0.4593          | 0.7907   |
| 0.2388        | 22.0  | 44   | 0.3441          | 0.8605   |
| 0.2388        | 23.0  | 46   | 0.5483          | 0.7907   |
| 0.2388        | 24.0  | 48   | 0.6399          | 0.7907   |
| 0.189         | 25.0  | 50   | 0.3333          | 0.8605   |
| 0.189         | 26.0  | 52   | 0.3326          | 0.8605   |
| 0.189         | 27.0  | 54   | 0.4150          | 0.7907   |
| 0.189         | 28.0  | 56   | 0.3420          | 0.8837   |
| 0.189         | 29.0  | 58   | 0.3649          | 0.8372   |
| 0.1718        | 30.0  | 60   | 0.3651          | 0.8605   |
| 0.1718        | 31.0  | 62   | 0.4676          | 0.8140   |
| 0.1718        | 32.0  | 64   | 0.3543          | 0.8605   |
| 0.1718        | 33.0  | 66   | 0.3209          | 0.8140   |
| 0.1718        | 34.0  | 68   | 0.3420          | 0.8605   |
| 0.1466        | 35.0  | 70   | 0.3800          | 0.8372   |
| 0.1466        | 36.0  | 72   | 0.6547          | 0.8140   |
| 0.1466        | 37.0  | 74   | 0.9743          | 0.7674   |
| 0.1466        | 38.0  | 76   | 0.6677          | 0.7907   |
| 0.1466        | 39.0  | 78   | 0.5691          | 0.8140   |
| 0.119         | 40.0  | 80   | 0.4796          | 0.8605   |
| 0.119         | 41.0  | 82   | 0.3243          | 0.8837   |
| 0.119         | 42.0  | 84   | 0.2969          | 0.8605   |
| 0.119         | 43.0  | 86   | 0.3637          | 0.8372   |
| 0.119         | 44.0  | 88   | 0.3098          | 0.9070   |
| 0.1123        | 45.0  | 90   | 0.3954          | 0.8837   |
| 0.1123        | 46.0  | 92   | 0.3197          | 0.9070   |
| 0.1123        | 47.0  | 94   | 0.3188          | 0.8372   |
| 0.1123        | 48.0  | 96   | 0.3100          | 0.8372   |
| 0.1123        | 49.0  | 98   | 0.3653          | 0.8605   |
| 0.1136        | 50.0  | 100  | 0.3527          | 0.8837   |
| 0.1136        | 51.0  | 102  | 0.3152          | 0.8605   |
| 0.1136        | 52.0  | 104  | 0.3277          | 0.8605   |
| 0.1136        | 53.0  | 106  | 0.3221          | 0.8837   |
| 0.1136        | 54.0  | 108  | 0.3438          | 0.8605   |
| 0.0858        | 55.0  | 110  | 0.4683          | 0.8605   |
| 0.0858        | 56.0  | 112  | 0.4511          | 0.8605   |
| 0.0858        | 57.0  | 114  | 0.3486          | 0.8605   |
| 0.0858        | 58.0  | 116  | 0.3594          | 0.8837   |
| 0.0858        | 59.0  | 118  | 0.3914          | 0.8605   |
| 0.084         | 60.0  | 120  | 0.4257          | 0.8837   |
| 0.084         | 61.0  | 122  | 0.4505          | 0.8837   |
| 0.084         | 62.0  | 124  | 0.4038          | 0.8605   |
| 0.084         | 63.0  | 126  | 0.3745          | 0.8372   |
| 0.084         | 64.0  | 128  | 0.3774          | 0.8140   |
| 0.0938        | 65.0  | 130  | 0.3712          | 0.8140   |
| 0.0938        | 66.0  | 132  | 0.3736          | 0.8140   |
| 0.0938        | 67.0  | 134  | 0.3840          | 0.8605   |
| 0.0938        | 68.0  | 136  | 0.3902          | 0.8605   |
| 0.0938        | 69.0  | 138  | 0.4105          | 0.8605   |
| 0.055         | 70.0  | 140  | 0.4498          | 0.8605   |
| 0.055         | 71.0  | 142  | 0.4954          | 0.8605   |
| 0.055         | 72.0  | 144  | 0.6397          | 0.8605   |
| 0.055         | 73.0  | 146  | 0.6271          | 0.8605   |
| 0.055         | 74.0  | 148  | 0.4821          | 0.8605   |
| 0.0755        | 75.0  | 150  | 0.3699          | 0.9070   |
| 0.0755        | 76.0  | 152  | 0.3303          | 0.8605   |
| 0.0755        | 77.0  | 154  | 0.3282          | 0.8837   |
| 0.0755        | 78.0  | 156  | 0.3181          | 0.8837   |
| 0.0755        | 79.0  | 158  | 0.3083          | 0.8605   |
| 0.0603        | 80.0  | 160  | 0.3170          | 0.8372   |
| 0.0603        | 81.0  | 162  | 0.3397          | 0.8372   |
| 0.0603        | 82.0  | 164  | 0.3538          | 0.8372   |
| 0.0603        | 83.0  | 166  | 0.3461          | 0.8372   |
| 0.0603        | 84.0  | 168  | 0.3337          | 0.8140   |
| 0.0653        | 85.0  | 170  | 0.3330          | 0.8372   |
| 0.0653        | 86.0  | 172  | 0.3451          | 0.8837   |
| 0.0653        | 87.0  | 174  | 0.3612          | 0.8837   |
| 0.0653        | 88.0  | 176  | 0.3822          | 0.8605   |
| 0.0653        | 89.0  | 178  | 0.3875          | 0.8605   |
| 0.0571        | 90.0  | 180  | 0.3845          | 0.8605   |
| 0.0571        | 91.0  | 182  | 0.3642          | 0.8837   |
| 0.0571        | 92.0  | 184  | 0.3529          | 0.8837   |
| 0.0571        | 93.0  | 186  | 0.3471          | 0.8837   |
| 0.0571        | 94.0  | 188  | 0.3540          | 0.8837   |
| 0.069         | 95.0  | 190  | 0.3609          | 0.8837   |
| 0.069         | 96.0  | 192  | 0.3609          | 0.8837   |
| 0.069         | 97.0  | 194  | 0.3634          | 0.8837   |
| 0.069         | 98.0  | 196  | 0.3627          | 0.8837   |
| 0.069         | 99.0  | 198  | 0.3609          | 0.8837   |
| 0.0667        | 100.0 | 200  | 0.3604          | 0.8837   |


### Framework versions

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

hasta normal