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# deit-base-distilled-patch16-224-75-fold4

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: 0.2564
- Accuracy: 0.9302

## 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    | 0.5613          | 0.6744   |
| No log        | 2.0   | 4    | 0.6233          | 0.6977   |
| No log        | 3.0   | 6    | 0.7506          | 0.6977   |
| No log        | 4.0   | 8    | 0.6462          | 0.6977   |
| 0.5231        | 5.0   | 10   | 0.3889          | 0.8140   |
| 0.5231        | 6.0   | 12   | 0.3485          | 0.8372   |
| 0.5231        | 7.0   | 14   | 0.4327          | 0.8372   |
| 0.5231        | 8.0   | 16   | 0.4470          | 0.8372   |
| 0.5231        | 9.0   | 18   | 0.3067          | 0.8605   |
| 0.3699        | 10.0  | 20   | 0.3075          | 0.8605   |
| 0.3699        | 11.0  | 22   | 0.5433          | 0.8372   |
| 0.3699        | 12.0  | 24   | 0.5849          | 0.8140   |
| 0.3699        | 13.0  | 26   | 0.4509          | 0.8372   |
| 0.3699        | 14.0  | 28   | 0.5410          | 0.8372   |
| 0.2951        | 15.0  | 30   | 0.4710          | 0.8140   |
| 0.2951        | 16.0  | 32   | 0.3738          | 0.8605   |
| 0.2951        | 17.0  | 34   | 0.3704          | 0.8372   |
| 0.2951        | 18.0  | 36   | 0.2682          | 0.9070   |
| 0.2951        | 19.0  | 38   | 0.4187          | 0.8372   |
| 0.2279        | 20.0  | 40   | 0.3961          | 0.8372   |
| 0.2279        | 21.0  | 42   | 0.2976          | 0.8837   |
| 0.2279        | 22.0  | 44   | 0.5655          | 0.8140   |
| 0.2279        | 23.0  | 46   | 0.4931          | 0.8372   |
| 0.2279        | 24.0  | 48   | 0.2672          | 0.9070   |
| 0.1823        | 25.0  | 50   | 0.2795          | 0.9070   |
| 0.1823        | 26.0  | 52   | 0.3885          | 0.8605   |
| 0.1823        | 27.0  | 54   | 0.2779          | 0.9070   |
| 0.1823        | 28.0  | 56   | 0.2990          | 0.8605   |
| 0.1823        | 29.0  | 58   | 0.3090          | 0.8605   |
| 0.1314        | 30.0  | 60   | 0.2564          | 0.9302   |
| 0.1314        | 31.0  | 62   | 0.2825          | 0.8605   |
| 0.1314        | 32.0  | 64   | 0.3655          | 0.8605   |
| 0.1314        | 33.0  | 66   | 0.2486          | 0.9302   |
| 0.1314        | 34.0  | 68   | 0.2734          | 0.9070   |
| 0.1387        | 35.0  | 70   | 0.5242          | 0.8372   |
| 0.1387        | 36.0  | 72   | 0.5958          | 0.8372   |
| 0.1387        | 37.0  | 74   | 0.3472          | 0.8837   |
| 0.1387        | 38.0  | 76   | 0.3057          | 0.9070   |
| 0.1387        | 39.0  | 78   | 0.4018          | 0.8372   |
| 0.1266        | 40.0  | 80   | 0.3940          | 0.8372   |
| 0.1266        | 41.0  | 82   | 0.3568          | 0.8605   |
| 0.1266        | 42.0  | 84   | 0.3533          | 0.8837   |
| 0.1266        | 43.0  | 86   | 0.3451          | 0.8837   |
| 0.1266        | 44.0  | 88   | 0.3478          | 0.9070   |
| 0.1004        | 45.0  | 90   | 0.3195          | 0.9302   |
| 0.1004        | 46.0  | 92   | 0.3926          | 0.9070   |
| 0.1004        | 47.0  | 94   | 0.4169          | 0.8837   |
| 0.1004        | 48.0  | 96   | 0.4274          | 0.8837   |
| 0.1004        | 49.0  | 98   | 0.4061          | 0.9070   |
| 0.1033        | 50.0  | 100  | 0.4277          | 0.8605   |
| 0.1033        | 51.0  | 102  | 0.3977          | 0.9070   |
| 0.1033        | 52.0  | 104  | 0.4428          | 0.8605   |
| 0.1033        | 53.0  | 106  | 0.6753          | 0.8140   |
| 0.1033        | 54.0  | 108  | 0.6912          | 0.8140   |
| 0.0827        | 55.0  | 110  | 0.4201          | 0.8605   |
| 0.0827        | 56.0  | 112  | 0.3086          | 0.9302   |
| 0.0827        | 57.0  | 114  | 0.3150          | 0.9302   |
| 0.0827        | 58.0  | 116  | 0.4757          | 0.8605   |
| 0.0827        | 59.0  | 118  | 0.6409          | 0.8605   |
| 0.0889        | 60.0  | 120  | 0.5430          | 0.8837   |
| 0.0889        | 61.0  | 122  | 0.4044          | 0.8837   |
| 0.0889        | 62.0  | 124  | 0.3473          | 0.9302   |
| 0.0889        | 63.0  | 126  | 0.3485          | 0.9302   |
| 0.0889        | 64.0  | 128  | 0.3711          | 0.9302   |
| 0.088         | 65.0  | 130  | 0.4405          | 0.8837   |
| 0.088         | 66.0  | 132  | 0.6526          | 0.8605   |
| 0.088         | 67.0  | 134  | 0.7019          | 0.8605   |
| 0.088         | 68.0  | 136  | 0.5408          | 0.8605   |
| 0.088         | 69.0  | 138  | 0.4057          | 0.9302   |
| 0.0734        | 70.0  | 140  | 0.3797          | 0.9302   |
| 0.0734        | 71.0  | 142  | 0.3811          | 0.9302   |
| 0.0734        | 72.0  | 144  | 0.4040          | 0.9302   |
| 0.0734        | 73.0  | 146  | 0.4567          | 0.8837   |
| 0.0734        | 74.0  | 148  | 0.5161          | 0.9070   |
| 0.0721        | 75.0  | 150  | 0.5240          | 0.8837   |
| 0.0721        | 76.0  | 152  | 0.5048          | 0.9070   |
| 0.0721        | 77.0  | 154  | 0.4635          | 0.9070   |
| 0.0721        | 78.0  | 156  | 0.4510          | 0.9070   |
| 0.0721        | 79.0  | 158  | 0.4931          | 0.9070   |
| 0.0592        | 80.0  | 160  | 0.5368          | 0.8837   |
| 0.0592        | 81.0  | 162  | 0.5297          | 0.8837   |
| 0.0592        | 82.0  | 164  | 0.4722          | 0.9070   |
| 0.0592        | 83.0  | 166  | 0.4179          | 0.9302   |
| 0.0592        | 84.0  | 168  | 0.4045          | 0.9302   |
| 0.0634        | 85.0  | 170  | 0.4200          | 0.9302   |
| 0.0634        | 86.0  | 172  | 0.4497          | 0.9302   |
| 0.0634        | 87.0  | 174  | 0.4796          | 0.9070   |
| 0.0634        | 88.0  | 176  | 0.4997          | 0.8837   |
| 0.0634        | 89.0  | 178  | 0.4867          | 0.8837   |
| 0.0758        | 90.0  | 180  | 0.4478          | 0.9302   |
| 0.0758        | 91.0  | 182  | 0.4145          | 0.9302   |
| 0.0758        | 92.0  | 184  | 0.4036          | 0.9302   |
| 0.0758        | 93.0  | 186  | 0.3877          | 0.9302   |
| 0.0758        | 94.0  | 188  | 0.3747          | 0.9070   |
| 0.0529        | 95.0  | 190  | 0.3685          | 0.9070   |
| 0.0529        | 96.0  | 192  | 0.3644          | 0.9070   |
| 0.0529        | 97.0  | 194  | 0.3648          | 0.9070   |
| 0.0529        | 98.0  | 196  | 0.3663          | 0.9070   |
| 0.0529        | 99.0  | 198  | 0.3686          | 0.9070   |
| 0.0594        | 100.0 | 200  | 0.3693          | 0.9070   |


### Framework versions

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

hasta normal