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# beit-base-patch16-224-75-fold2

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2685
- 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    | 0.7091          | 0.5349   |
| No log        | 2.0   | 4    | 0.6502          | 0.7209   |
| No log        | 3.0   | 6    | 0.9193          | 0.6977   |
| No log        | 4.0   | 8    | 0.7499          | 0.7442   |
| 0.6436        | 5.0   | 10   | 0.4527          | 0.8140   |
| 0.6436        | 6.0   | 12   | 0.4169          | 0.8372   |
| 0.6436        | 7.0   | 14   | 0.5773          | 0.7442   |
| 0.6436        | 8.0   | 16   | 0.4076          | 0.8605   |
| 0.6436        | 9.0   | 18   | 0.3939          | 0.8605   |
| 0.3863        | 10.0  | 20   | 0.4017          | 0.8605   |
| 0.3863        | 11.0  | 22   | 0.4918          | 0.8140   |
| 0.3863        | 12.0  | 24   | 0.2688          | 0.8372   |
| 0.3863        | 13.0  | 26   | 0.3884          | 0.8140   |
| 0.3863        | 14.0  | 28   | 0.3679          | 0.8140   |
| 0.2925        | 15.0  | 30   | 0.2802          | 0.8837   |
| 0.2925        | 16.0  | 32   | 0.2436          | 0.9070   |
| 0.2925        | 17.0  | 34   | 0.2337          | 0.9302   |
| 0.2925        | 18.0  | 36   | 0.3711          | 0.8140   |
| 0.2925        | 19.0  | 38   | 0.2372          | 0.9302   |
| 0.2289        | 20.0  | 40   | 0.2685          | 0.9535   |
| 0.2289        | 21.0  | 42   | 0.2610          | 0.9070   |
| 0.2289        | 22.0  | 44   | 0.3328          | 0.8372   |
| 0.2289        | 23.0  | 46   | 0.3479          | 0.8372   |
| 0.2289        | 24.0  | 48   | 0.2855          | 0.8837   |
| 0.219         | 25.0  | 50   | 0.2962          | 0.9070   |
| 0.219         | 26.0  | 52   | 0.4038          | 0.9070   |
| 0.219         | 27.0  | 54   | 0.3149          | 0.9070   |
| 0.219         | 28.0  | 56   | 0.3212          | 0.9070   |
| 0.219         | 29.0  | 58   | 0.4895          | 0.8605   |
| 0.1933        | 30.0  | 60   | 0.4335          | 0.8837   |
| 0.1933        | 31.0  | 62   | 0.3521          | 0.8372   |
| 0.1933        | 32.0  | 64   | 0.2960          | 0.8837   |
| 0.1933        | 33.0  | 66   | 0.4037          | 0.8372   |
| 0.1933        | 34.0  | 68   | 0.2913          | 0.8837   |
| 0.1892        | 35.0  | 70   | 0.3043          | 0.8837   |
| 0.1892        | 36.0  | 72   | 0.3602          | 0.9302   |
| 0.1892        | 37.0  | 74   | 0.3315          | 0.9302   |
| 0.1892        | 38.0  | 76   | 0.2674          | 0.9302   |
| 0.1892        | 39.0  | 78   | 0.2970          | 0.9535   |
| 0.15          | 40.0  | 80   | 0.2661          | 0.9535   |
| 0.15          | 41.0  | 82   | 0.2551          | 0.8837   |
| 0.15          | 42.0  | 84   | 0.2467          | 0.9302   |
| 0.15          | 43.0  | 86   | 0.3008          | 0.9535   |
| 0.15          | 44.0  | 88   | 0.3265          | 0.9302   |
| 0.1238        | 45.0  | 90   | 0.2668          | 0.9302   |
| 0.1238        | 46.0  | 92   | 0.2574          | 0.9302   |
| 0.1238        | 47.0  | 94   | 0.2498          | 0.9535   |
| 0.1238        | 48.0  | 96   | 0.3319          | 0.8837   |
| 0.1238        | 49.0  | 98   | 0.2358          | 0.9302   |
| 0.1063        | 50.0  | 100  | 0.2015          | 0.9302   |
| 0.1063        | 51.0  | 102  | 0.2171          | 0.9302   |
| 0.1063        | 52.0  | 104  | 0.3119          | 0.9302   |
| 0.1063        | 53.0  | 106  | 0.2674          | 0.9070   |
| 0.1063        | 54.0  | 108  | 0.3076          | 0.8837   |
| 0.1112        | 55.0  | 110  | 0.3182          | 0.8837   |
| 0.1112        | 56.0  | 112  | 0.3371          | 0.9070   |
| 0.1112        | 57.0  | 114  | 0.3540          | 0.9070   |
| 0.1112        | 58.0  | 116  | 0.4058          | 0.9070   |
| 0.1112        | 59.0  | 118  | 0.4013          | 0.9070   |
| 0.1128        | 60.0  | 120  | 0.3309          | 0.9302   |
| 0.1128        | 61.0  | 122  | 0.3272          | 0.9302   |
| 0.1128        | 62.0  | 124  | 0.4012          | 0.9070   |
| 0.1128        | 63.0  | 126  | 0.5794          | 0.8605   |
| 0.1128        | 64.0  | 128  | 0.3881          | 0.9070   |
| 0.1168        | 65.0  | 130  | 0.2990          | 0.9070   |
| 0.1168        | 66.0  | 132  | 0.3018          | 0.8837   |
| 0.1168        | 67.0  | 134  | 0.2561          | 0.9302   |
| 0.1168        | 68.0  | 136  | 0.2921          | 0.9302   |
| 0.1168        | 69.0  | 138  | 0.3258          | 0.9070   |
| 0.0846        | 70.0  | 140  | 0.2925          | 0.9302   |
| 0.0846        | 71.0  | 142  | 0.3073          | 0.9302   |
| 0.0846        | 72.0  | 144  | 0.3318          | 0.9302   |
| 0.0846        | 73.0  | 146  | 0.3427          | 0.9302   |
| 0.0846        | 74.0  | 148  | 0.3588          | 0.9070   |
| 0.0845        | 75.0  | 150  | 0.3939          | 0.9070   |
| 0.0845        | 76.0  | 152  | 0.3774          | 0.9070   |
| 0.0845        | 77.0  | 154  | 0.3746          | 0.9070   |
| 0.0845        | 78.0  | 156  | 0.4073          | 0.8837   |
| 0.0845        | 79.0  | 158  | 0.3886          | 0.9070   |
| 0.0885        | 80.0  | 160  | 0.3765          | 0.9070   |
| 0.0885        | 81.0  | 162  | 0.3977          | 0.9070   |
| 0.0885        | 82.0  | 164  | 0.3864          | 0.9070   |
| 0.0885        | 83.0  | 166  | 0.3809          | 0.9070   |
| 0.0885        | 84.0  | 168  | 0.4492          | 0.8605   |
| 0.0859        | 85.0  | 170  | 0.5479          | 0.8605   |
| 0.0859        | 86.0  | 172  | 0.5372          | 0.8605   |
| 0.0859        | 87.0  | 174  | 0.4512          | 0.8605   |
| 0.0859        | 88.0  | 176  | 0.3930          | 0.9070   |
| 0.0859        | 89.0  | 178  | 0.3842          | 0.9302   |
| 0.0764        | 90.0  | 180  | 0.3808          | 0.9302   |
| 0.0764        | 91.0  | 182  | 0.3787          | 0.9302   |
| 0.0764        | 92.0  | 184  | 0.3833          | 0.9070   |
| 0.0764        | 93.0  | 186  | 0.3912          | 0.9070   |
| 0.0764        | 94.0  | 188  | 0.3888          | 0.8837   |
| 0.0727        | 95.0  | 190  | 0.3817          | 0.8837   |
| 0.0727        | 96.0  | 192  | 0.3708          | 0.9070   |
| 0.0727        | 97.0  | 194  | 0.3640          | 0.9070   |
| 0.0727        | 98.0  | 196  | 0.3613          | 0.9302   |
| 0.0727        | 99.0  | 198  | 0.3607          | 0.9302   |
| 0.069         | 100.0 | 200  | 0.3605          | 0.9302   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

hasta normal