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# beit-base-patch16-224-75-fold3

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: 0.3326
- 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    | 0.5223          | 0.8140   |
| No log        | 2.0   | 4    | 0.5171          | 0.7907   |
| No log        | 3.0   | 6    | 0.5562          | 0.7907   |
| No log        | 4.0   | 8    | 0.5032          | 0.7907   |
| 0.5026        | 5.0   | 10   | 0.4636          | 0.7907   |
| 0.5026        | 6.0   | 12   | 0.4143          | 0.8372   |
| 0.5026        | 7.0   | 14   | 0.4338          | 0.8372   |
| 0.5026        | 8.0   | 16   | 0.4471          | 0.8372   |
| 0.5026        | 9.0   | 18   | 0.3882          | 0.8140   |
| 0.3578        | 10.0  | 20   | 0.3487          | 0.8372   |
| 0.3578        | 11.0  | 22   | 0.3975          | 0.8372   |
| 0.3578        | 12.0  | 24   | 0.3497          | 0.8372   |
| 0.3578        | 13.0  | 26   | 0.4257          | 0.8372   |
| 0.3578        | 14.0  | 28   | 0.2964          | 0.8837   |
| 0.322         | 15.0  | 30   | 0.4666          | 0.8372   |
| 0.322         | 16.0  | 32   | 0.3815          | 0.7907   |
| 0.322         | 17.0  | 34   | 0.2859          | 0.8837   |
| 0.322         | 18.0  | 36   | 0.5995          | 0.8140   |
| 0.322         | 19.0  | 38   | 0.4421          | 0.8372   |
| 0.2983        | 20.0  | 40   | 0.2857          | 0.9070   |
| 0.2983        | 21.0  | 42   | 0.3963          | 0.8605   |
| 0.2983        | 22.0  | 44   | 0.2775          | 0.9302   |
| 0.2983        | 23.0  | 46   | 0.4763          | 0.7442   |
| 0.2983        | 24.0  | 48   | 0.2789          | 0.8605   |
| 0.2545        | 25.0  | 50   | 0.3596          | 0.8372   |
| 0.2545        | 26.0  | 52   | 0.2447          | 0.9302   |
| 0.2545        | 27.0  | 54   | 0.4725          | 0.8140   |
| 0.2545        | 28.0  | 56   | 0.3389          | 0.8837   |
| 0.2545        | 29.0  | 58   | 0.3472          | 0.8140   |
| 0.1914        | 30.0  | 60   | 0.3326          | 0.9535   |
| 0.1914        | 31.0  | 62   | 0.2856          | 0.9302   |
| 0.1914        | 32.0  | 64   | 0.2857          | 0.8605   |
| 0.1914        | 33.0  | 66   | 0.2460          | 0.9302   |
| 0.1914        | 34.0  | 68   | 0.5097          | 0.8140   |
| 0.1782        | 35.0  | 70   | 0.2503          | 0.9535   |
| 0.1782        | 36.0  | 72   | 0.2338          | 0.9302   |
| 0.1782        | 37.0  | 74   | 0.4161          | 0.8837   |
| 0.1782        | 38.0  | 76   | 0.3522          | 0.9070   |
| 0.1782        | 39.0  | 78   | 0.2566          | 0.8372   |
| 0.1435        | 40.0  | 80   | 0.2658          | 0.8605   |
| 0.1435        | 41.0  | 82   | 0.2197          | 0.9302   |
| 0.1435        | 42.0  | 84   | 0.2200          | 0.8837   |
| 0.1435        | 43.0  | 86   | 0.2167          | 0.8837   |
| 0.1435        | 44.0  | 88   | 0.3905          | 0.9070   |
| 0.1131        | 45.0  | 90   | 0.5501          | 0.8837   |
| 0.1131        | 46.0  | 92   | 0.5251          | 0.9070   |
| 0.1131        | 47.0  | 94   | 0.4207          | 0.9070   |
| 0.1131        | 48.0  | 96   | 0.3181          | 0.8605   |
| 0.1131        | 49.0  | 98   | 0.4059          | 0.8140   |
| 0.1397        | 50.0  | 100  | 0.3061          | 0.8605   |
| 0.1397        | 51.0  | 102  | 0.2825          | 0.9302   |
| 0.1397        | 52.0  | 104  | 0.2886          | 0.9302   |
| 0.1397        | 53.0  | 106  | 0.3140          | 0.9070   |
| 0.1397        | 54.0  | 108  | 0.3363          | 0.9070   |
| 0.1048        | 55.0  | 110  | 0.3388          | 0.8837   |
| 0.1048        | 56.0  | 112  | 0.3391          | 0.9070   |
| 0.1048        | 57.0  | 114  | 0.3927          | 0.8605   |
| 0.1048        | 58.0  | 116  | 0.3799          | 0.8837   |
| 0.1048        | 59.0  | 118  | 0.5491          | 0.8140   |
| 0.1013        | 60.0  | 120  | 0.6218          | 0.8140   |
| 0.1013        | 61.0  | 122  | 0.4467          | 0.8372   |
| 0.1013        | 62.0  | 124  | 0.3842          | 0.9302   |
| 0.1013        | 63.0  | 126  | 0.3517          | 0.9302   |
| 0.1013        | 64.0  | 128  | 0.3083          | 0.9070   |
| 0.0987        | 65.0  | 130  | 0.3125          | 0.9535   |
| 0.0987        | 66.0  | 132  | 0.3968          | 0.9070   |
| 0.0987        | 67.0  | 134  | 0.3974          | 0.9070   |
| 0.0987        | 68.0  | 136  | 0.3304          | 0.9302   |
| 0.0987        | 69.0  | 138  | 0.3508          | 0.9070   |
| 0.0932        | 70.0  | 140  | 0.3760          | 0.9070   |
| 0.0932        | 71.0  | 142  | 0.3828          | 0.9535   |
| 0.0932        | 72.0  | 144  | 0.3291          | 0.9302   |
| 0.0932        | 73.0  | 146  | 0.3109          | 0.8837   |
| 0.0932        | 74.0  | 148  | 0.3078          | 0.9302   |
| 0.0917        | 75.0  | 150  | 0.4478          | 0.9070   |
| 0.0917        | 76.0  | 152  | 0.6594          | 0.8605   |
| 0.0917        | 77.0  | 154  | 0.6036          | 0.8605   |
| 0.0917        | 78.0  | 156  | 0.3920          | 0.9070   |
| 0.0917        | 79.0  | 158  | 0.3263          | 0.8605   |
| 0.0917        | 80.0  | 160  | 0.3551          | 0.8372   |
| 0.0917        | 81.0  | 162  | 0.3475          | 0.8372   |
| 0.0917        | 82.0  | 164  | 0.3338          | 0.8837   |
| 0.0917        | 83.0  | 166  | 0.3354          | 0.9070   |
| 0.0917        | 84.0  | 168  | 0.3611          | 0.9070   |
| 0.0901        | 85.0  | 170  | 0.3558          | 0.9070   |
| 0.0901        | 86.0  | 172  | 0.3412          | 0.8837   |
| 0.0901        | 87.0  | 174  | 0.3352          | 0.9302   |
| 0.0901        | 88.0  | 176  | 0.3627          | 0.9070   |
| 0.0901        | 89.0  | 178  | 0.3637          | 0.9070   |
| 0.0764        | 90.0  | 180  | 0.3369          | 0.9070   |
| 0.0764        | 91.0  | 182  | 0.3246          | 0.9302   |
| 0.0764        | 92.0  | 184  | 0.3208          | 0.9302   |
| 0.0764        | 93.0  | 186  | 0.3229          | 0.9302   |
| 0.0764        | 94.0  | 188  | 0.3326          | 0.9302   |
| 0.0664        | 95.0  | 190  | 0.3340          | 0.9302   |
| 0.0664        | 96.0  | 192  | 0.3296          | 0.9302   |
| 0.0664        | 97.0  | 194  | 0.3268          | 0.9302   |
| 0.0664        | 98.0  | 196  | 0.3260          | 0.9535   |
| 0.0664        | 99.0  | 198  | 0.3288          | 0.9535   |
| 0.057         | 100.0 | 200  | 0.3298          | 0.9535   |


### Framework versions

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

hasta normal