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# deit-base-distilled-patch16-224-75-fold5

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: 0.2788
- Accuracy: 0.9535

## 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    | 0.8041          | 0.3488   |
| No log        | 2.0   | 4    | 0.6042          | 0.7209   |
| No log        | 3.0   | 6    | 0.6582          | 0.6977   |
| No log        | 4.0   | 8    | 0.7007          | 0.6977   |
| 0.6827        | 5.0   | 10   | 0.5653          | 0.6977   |
| 0.6827        | 6.0   | 12   | 0.4345          | 0.8140   |
| 0.6827        | 7.0   | 14   | 0.4493          | 0.8140   |
| 0.6827        | 8.0   | 16   | 0.4764          | 0.8140   |
| 0.6827        | 9.0   | 18   | 0.3636          | 0.8372   |
| 0.4147        | 10.0  | 20   | 0.2769          | 0.8605   |
| 0.4147        | 11.0  | 22   | 0.3538          | 0.8372   |
| 0.4147        | 12.0  | 24   | 0.3320          | 0.8605   |
| 0.4147        | 13.0  | 26   | 0.2668          | 0.8372   |
| 0.4147        | 14.0  | 28   | 0.2823          | 0.8140   |
| 0.3405        | 15.0  | 30   | 0.4864          | 0.8372   |
| 0.3405        | 16.0  | 32   | 0.4416          | 0.8372   |
| 0.3405        | 17.0  | 34   | 0.3585          | 0.8140   |
| 0.3405        | 18.0  | 36   | 0.4769          | 0.8372   |
| 0.3405        | 19.0  | 38   | 0.5918          | 0.8372   |
| 0.3074        | 20.0  | 40   | 0.4116          | 0.8605   |
| 0.3074        | 21.0  | 42   | 0.4578          | 0.8372   |
| 0.3074        | 22.0  | 44   | 0.4294          | 0.8605   |
| 0.3074        | 23.0  | 46   | 0.5401          | 0.8372   |
| 0.3074        | 24.0  | 48   | 0.2069          | 0.8837   |
| 0.2155        | 25.0  | 50   | 0.2216          | 0.8837   |
| 0.2155        | 26.0  | 52   | 0.3837          | 0.8837   |
| 0.2155        | 27.0  | 54   | 0.2473          | 0.9070   |
| 0.2155        | 28.0  | 56   | 0.2233          | 0.9070   |
| 0.2155        | 29.0  | 58   | 0.3489          | 0.8837   |
| 0.1874        | 30.0  | 60   | 0.4635          | 0.8605   |
| 0.1874        | 31.0  | 62   | 0.2491          | 0.8837   |
| 0.1874        | 32.0  | 64   | 0.2564          | 0.8837   |
| 0.1874        | 33.0  | 66   | 0.3655          | 0.8837   |
| 0.1874        | 34.0  | 68   | 0.2498          | 0.9302   |
| 0.1324        | 35.0  | 70   | 0.2922          | 0.8837   |
| 0.1324        | 36.0  | 72   | 0.5368          | 0.8837   |
| 0.1324        | 37.0  | 74   | 0.5739          | 0.8837   |
| 0.1324        | 38.0  | 76   | 0.5049          | 0.8837   |
| 0.1324        | 39.0  | 78   | 0.5903          | 0.8837   |
| 0.1222        | 40.0  | 80   | 0.4886          | 0.8837   |
| 0.1222        | 41.0  | 82   | 0.4174          | 0.8837   |
| 0.1222        | 42.0  | 84   | 0.5429          | 0.8605   |
| 0.1222        | 43.0  | 86   | 0.6897          | 0.8605   |
| 0.1222        | 44.0  | 88   | 0.6805          | 0.8605   |
| 0.1008        | 45.0  | 90   | 0.4073          | 0.8837   |
| 0.1008        | 46.0  | 92   | 0.4161          | 0.8837   |
| 0.1008        | 47.0  | 94   | 0.6485          | 0.8837   |
| 0.1008        | 48.0  | 96   | 0.6746          | 0.8837   |
| 0.1008        | 49.0  | 98   | 0.4433          | 0.8837   |
| 0.117         | 50.0  | 100  | 0.2788          | 0.9535   |
| 0.117         | 51.0  | 102  | 0.3441          | 0.8837   |
| 0.117         | 52.0  | 104  | 0.4663          | 0.8605   |
| 0.117         | 53.0  | 106  | 0.3300          | 0.9070   |
| 0.117         | 54.0  | 108  | 0.2531          | 0.9535   |
| 0.0879        | 55.0  | 110  | 0.2261          | 0.9535   |
| 0.0879        | 56.0  | 112  | 0.3490          | 0.9070   |
| 0.0879        | 57.0  | 114  | 0.5060          | 0.8837   |
| 0.0879        | 58.0  | 116  | 0.4847          | 0.8837   |
| 0.0879        | 59.0  | 118  | 0.3604          | 0.8837   |
| 0.0898        | 60.0  | 120  | 0.3626          | 0.8837   |
| 0.0898        | 61.0  | 122  | 0.4641          | 0.8837   |
| 0.0898        | 62.0  | 124  | 0.4606          | 0.8837   |
| 0.0898        | 63.0  | 126  | 0.3419          | 0.8837   |
| 0.0898        | 64.0  | 128  | 0.3331          | 0.9070   |
| 0.095         | 65.0  | 130  | 0.3804          | 0.8837   |
| 0.095         | 66.0  | 132  | 0.3783          | 0.8837   |
| 0.095         | 67.0  | 134  | 0.3524          | 0.8837   |
| 0.095         | 68.0  | 136  | 0.4232          | 0.8605   |
| 0.095         | 69.0  | 138  | 0.4157          | 0.8605   |
| 0.0735        | 70.0  | 140  | 0.4937          | 0.8605   |
| 0.0735        | 71.0  | 142  | 0.4616          | 0.8605   |
| 0.0735        | 72.0  | 144  | 0.4388          | 0.8837   |
| 0.0735        | 73.0  | 146  | 0.3811          | 0.8837   |
| 0.0735        | 74.0  | 148  | 0.2522          | 0.9302   |
| 0.0855        | 75.0  | 150  | 0.1677          | 0.9535   |
| 0.0855        | 76.0  | 152  | 0.1653          | 0.9535   |
| 0.0855        | 77.0  | 154  | 0.2173          | 0.9535   |
| 0.0855        | 78.0  | 156  | 0.3596          | 0.8837   |
| 0.0855        | 79.0  | 158  | 0.4653          | 0.8605   |
| 0.077         | 80.0  | 160  | 0.4739          | 0.8837   |
| 0.077         | 81.0  | 162  | 0.3609          | 0.8837   |
| 0.077         | 82.0  | 164  | 0.2334          | 0.9535   |
| 0.077         | 83.0  | 166  | 0.2125          | 0.9535   |
| 0.077         | 84.0  | 168  | 0.2595          | 0.9535   |
| 0.0731        | 85.0  | 170  | 0.3702          | 0.8837   |
| 0.0731        | 86.0  | 172  | 0.4634          | 0.8837   |
| 0.0731        | 87.0  | 174  | 0.5254          | 0.8837   |
| 0.0731        | 88.0  | 176  | 0.5813          | 0.8837   |
| 0.0731        | 89.0  | 178  | 0.5628          | 0.8837   |
| 0.078         | 90.0  | 180  | 0.5479          | 0.8837   |
| 0.078         | 91.0  | 182  | 0.5216          | 0.8837   |
| 0.078         | 92.0  | 184  | 0.5098          | 0.8837   |
| 0.078         | 93.0  | 186  | 0.5244          | 0.8837   |
| 0.078         | 94.0  | 188  | 0.5472          | 0.8837   |
| 0.079         | 95.0  | 190  | 0.5590          | 0.8837   |
| 0.079         | 96.0  | 192  | 0.5589          | 0.8837   |
| 0.079         | 97.0  | 194  | 0.5455          | 0.8837   |
| 0.079         | 98.0  | 196  | 0.5322          | 0.8837   |
| 0.079         | 99.0  | 198  | 0.5215          | 0.8837   |
| 0.0477        | 100.0 | 200  | 0.5202          | 0.8837   |


### Framework versions

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

hasta normal