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# beit-base-patch16-224-65-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: 0.5711
- Accuracy: 0.8592

## 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        | 0.9231  | 3    | 0.8549          | 0.5211   |
| No log        | 1.8462  | 6    | 0.6976          | 0.5634   |
| No log        | 2.7692  | 9    | 0.6809          | 0.5634   |
| 0.7778        | 4.0     | 13   | 0.6459          | 0.6056   |
| 0.7778        | 4.9231  | 16   | 0.6353          | 0.6338   |
| 0.7778        | 5.8462  | 19   | 0.6141          | 0.6197   |
| 0.6542        | 6.7692  | 22   | 0.6003          | 0.6056   |
| 0.6542        | 8.0     | 26   | 0.6168          | 0.6761   |
| 0.6542        | 8.9231  | 29   | 0.5781          | 0.6901   |
| 0.5817        | 9.8462  | 32   | 0.5710          | 0.7324   |
| 0.5817        | 10.7692 | 35   | 0.5345          | 0.7465   |
| 0.5817        | 12.0    | 39   | 0.6058          | 0.6479   |
| 0.513         | 12.9231 | 42   | 0.6433          | 0.7042   |
| 0.513         | 13.8462 | 45   | 0.5830          | 0.7042   |
| 0.513         | 14.7692 | 48   | 0.6167          | 0.7042   |
| 0.4756        | 16.0    | 52   | 0.7304          | 0.6338   |
| 0.4756        | 16.9231 | 55   | 0.5485          | 0.7606   |
| 0.4756        | 17.8462 | 58   | 0.5166          | 0.7606   |
| 0.4123        | 18.7692 | 61   | 0.6267          | 0.7746   |
| 0.4123        | 20.0    | 65   | 0.4253          | 0.8169   |
| 0.4123        | 20.9231 | 68   | 0.4698          | 0.7746   |
| 0.3745        | 21.8462 | 71   | 0.5312          | 0.7887   |
| 0.3745        | 22.7692 | 74   | 0.5158          | 0.7465   |
| 0.3745        | 24.0    | 78   | 0.5969          | 0.8028   |
| 0.3751        | 24.9231 | 81   | 0.5419          | 0.7606   |
| 0.3751        | 25.8462 | 84   | 0.4630          | 0.8028   |
| 0.3751        | 26.7692 | 87   | 0.5367          | 0.8028   |
| 0.3079        | 28.0    | 91   | 0.5220          | 0.8310   |
| 0.3079        | 28.9231 | 94   | 0.5342          | 0.7887   |
| 0.3079        | 29.8462 | 97   | 0.5711          | 0.8592   |
| 0.2831        | 30.7692 | 100  | 0.5757          | 0.7606   |
| 0.2831        | 32.0    | 104  | 0.5200          | 0.7465   |
| 0.2831        | 32.9231 | 107  | 0.4496          | 0.8451   |
| 0.292         | 33.8462 | 110  | 0.6480          | 0.8169   |
| 0.292         | 34.7692 | 113  | 0.6956          | 0.7465   |
| 0.292         | 36.0    | 117  | 0.5629          | 0.8169   |
| 0.2712        | 36.9231 | 120  | 0.7614          | 0.6901   |
| 0.2712        | 37.8462 | 123  | 0.5625          | 0.8028   |
| 0.2712        | 38.7692 | 126  | 0.5711          | 0.7746   |
| 0.2447        | 40.0    | 130  | 0.5476          | 0.7746   |
| 0.2447        | 40.9231 | 133  | 0.5354          | 0.8028   |
| 0.2447        | 41.8462 | 136  | 0.5217          | 0.8169   |
| 0.2447        | 42.7692 | 139  | 0.5767          | 0.8028   |
| 0.185         | 44.0    | 143  | 0.5606          | 0.8169   |
| 0.185         | 44.9231 | 146  | 0.6719          | 0.7887   |
| 0.185         | 45.8462 | 149  | 0.6074          | 0.7887   |
| 0.1921        | 46.7692 | 152  | 0.6351          | 0.7746   |
| 0.1921        | 48.0    | 156  | 0.5916          | 0.7746   |
| 0.1921        | 48.9231 | 159  | 0.6103          | 0.7887   |
| 0.1844        | 49.8462 | 162  | 0.5758          | 0.7887   |
| 0.1844        | 50.7692 | 165  | 0.5497          | 0.8169   |
| 0.1844        | 52.0    | 169  | 0.5377          | 0.8310   |
| 0.17          | 52.9231 | 172  | 0.6279          | 0.8169   |
| 0.17          | 53.8462 | 175  | 0.5826          | 0.7887   |
| 0.17          | 54.7692 | 178  | 0.7173          | 0.7746   |
| 0.1724        | 56.0    | 182  | 0.5340          | 0.8451   |
| 0.1724        | 56.9231 | 185  | 0.5528          | 0.8592   |
| 0.1724        | 57.8462 | 188  | 0.6547          | 0.7887   |
| 0.1734        | 58.7692 | 191  | 0.5986          | 0.8310   |
| 0.1734        | 60.0    | 195  | 0.6057          | 0.8028   |
| 0.1734        | 60.9231 | 198  | 0.7183          | 0.8028   |
| 0.1582        | 61.8462 | 201  | 0.5912          | 0.8169   |
| 0.1582        | 62.7692 | 204  | 0.6002          | 0.8028   |
| 0.1582        | 64.0    | 208  | 0.7886          | 0.7606   |
| 0.1372        | 64.9231 | 211  | 0.7019          | 0.7887   |
| 0.1372        | 65.8462 | 214  | 0.6460          | 0.8169   |
| 0.1372        | 66.7692 | 217  | 0.6935          | 0.8028   |
| 0.153         | 68.0    | 221  | 0.8108          | 0.7746   |
| 0.153         | 68.9231 | 224  | 0.7539          | 0.7887   |
| 0.153         | 69.8462 | 227  | 0.7090          | 0.7746   |
| 0.1512        | 70.7692 | 230  | 0.7147          | 0.7887   |
| 0.1512        | 72.0    | 234  | 0.8680          | 0.8028   |
| 0.1512        | 72.9231 | 237  | 0.8785          | 0.7887   |
| 0.1381        | 73.8462 | 240  | 0.7413          | 0.7887   |
| 0.1381        | 74.7692 | 243  | 0.7255          | 0.8169   |
| 0.1381        | 76.0    | 247  | 0.7124          | 0.7887   |
| 0.1432        | 76.9231 | 250  | 0.7343          | 0.8028   |
| 0.1432        | 77.8462 | 253  | 0.7404          | 0.8028   |
| 0.1432        | 78.7692 | 256  | 0.6941          | 0.7887   |
| 0.1135        | 80.0    | 260  | 0.6721          | 0.8310   |
| 0.1135        | 80.9231 | 263  | 0.6692          | 0.8310   |
| 0.1135        | 81.8462 | 266  | 0.6880          | 0.8028   |
| 0.1135        | 82.7692 | 269  | 0.6857          | 0.8028   |
| 0.1182        | 84.0    | 273  | 0.6850          | 0.7887   |
| 0.1182        | 84.9231 | 276  | 0.6816          | 0.7887   |
| 0.1182        | 85.8462 | 279  | 0.7048          | 0.7746   |
| 0.1019        | 86.7692 | 282  | 0.7804          | 0.7746   |
| 0.1019        | 88.0    | 286  | 0.8013          | 0.7746   |
| 0.1019        | 88.9231 | 289  | 0.7506          | 0.7606   |
| 0.1163        | 89.8462 | 292  | 0.7047          | 0.7746   |
| 0.1163        | 90.7692 | 295  | 0.6763          | 0.8028   |
| 0.1163        | 92.0    | 299  | 0.6606          | 0.8028   |
| 0.1258        | 92.3077 | 300  | 0.6592          | 0.8028   |


### Framework versions

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

hasta normal