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# beit-base-patch16-224-65-fold4

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: 0.5415
- Accuracy: 0.8732

## 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    | 0.7415          | 0.5352   |
| No log        | 1.8462  | 6    | 0.7177          | 0.4507   |
| No log        | 2.7692  | 9    | 0.6709          | 0.6056   |
| 0.748         | 4.0     | 13   | 0.6333          | 0.6338   |
| 0.748         | 4.9231  | 16   | 0.6162          | 0.7324   |
| 0.748         | 5.8462  | 19   | 0.6303          | 0.6338   |
| 0.6397        | 6.7692  | 22   | 0.5950          | 0.6761   |
| 0.6397        | 8.0     | 26   | 0.6325          | 0.6056   |
| 0.6397        | 8.9231  | 29   | 0.5799          | 0.7042   |
| 0.5957        | 9.8462  | 32   | 0.5793          | 0.6901   |
| 0.5957        | 10.7692 | 35   | 0.5869          | 0.7183   |
| 0.5957        | 12.0    | 39   | 0.6195          | 0.5775   |
| 0.5676        | 12.9231 | 42   | 0.5940          | 0.6479   |
| 0.5676        | 13.8462 | 45   | 0.6612          | 0.6197   |
| 0.5676        | 14.7692 | 48   | 0.5598          | 0.7465   |
| 0.5952        | 16.0    | 52   | 0.5472          | 0.7465   |
| 0.5952        | 16.9231 | 55   | 0.4823          | 0.7887   |
| 0.5952        | 17.8462 | 58   | 0.6493          | 0.6901   |
| 0.4908        | 18.7692 | 61   | 0.5539          | 0.7465   |
| 0.4908        | 20.0    | 65   | 0.5406          | 0.7606   |
| 0.4908        | 20.9231 | 68   | 0.5443          | 0.7606   |
| 0.4474        | 21.8462 | 71   | 0.6548          | 0.7042   |
| 0.4474        | 22.7692 | 74   | 0.4924          | 0.7746   |
| 0.4474        | 24.0    | 78   | 0.4671          | 0.8169   |
| 0.4106        | 24.9231 | 81   | 0.4117          | 0.8310   |
| 0.4106        | 25.8462 | 84   | 0.4630          | 0.8592   |
| 0.4106        | 26.7692 | 87   | 0.4915          | 0.8310   |
| 0.3163        | 28.0    | 91   | 0.6336          | 0.8028   |
| 0.3163        | 28.9231 | 94   | 0.5920          | 0.7887   |
| 0.3163        | 29.8462 | 97   | 0.5653          | 0.8028   |
| 0.3234        | 30.7692 | 100  | 0.6411          | 0.7746   |
| 0.3234        | 32.0    | 104  | 0.6728          | 0.7887   |
| 0.3234        | 32.9231 | 107  | 0.5503          | 0.8028   |
| 0.2969        | 33.8462 | 110  | 0.4914          | 0.8310   |
| 0.2969        | 34.7692 | 113  | 0.5952          | 0.8169   |
| 0.2969        | 36.0    | 117  | 0.7161          | 0.7746   |
| 0.2325        | 36.9231 | 120  | 0.6517          | 0.7746   |
| 0.2325        | 37.8462 | 123  | 0.5832          | 0.7887   |
| 0.2325        | 38.7692 | 126  | 0.6309          | 0.7746   |
| 0.2447        | 40.0    | 130  | 0.8011          | 0.7465   |
| 0.2447        | 40.9231 | 133  | 0.6085          | 0.7887   |
| 0.2447        | 41.8462 | 136  | 0.6470          | 0.7606   |
| 0.2447        | 42.7692 | 139  | 0.7744          | 0.7746   |
| 0.2217        | 44.0    | 143  | 0.5730          | 0.8310   |
| 0.2217        | 44.9231 | 146  | 0.5577          | 0.8169   |
| 0.2217        | 45.8462 | 149  | 0.5226          | 0.8451   |
| 0.2231        | 46.7692 | 152  | 0.5115          | 0.8310   |
| 0.2231        | 48.0    | 156  | 0.5415          | 0.8732   |
| 0.2231        | 48.9231 | 159  | 0.5971          | 0.8310   |
| 0.2014        | 49.8462 | 162  | 0.8717          | 0.7606   |
| 0.2014        | 50.7692 | 165  | 0.7063          | 0.7887   |
| 0.2014        | 52.0    | 169  | 0.6917          | 0.7887   |
| 0.1827        | 52.9231 | 172  | 0.6880          | 0.7887   |
| 0.1827        | 53.8462 | 175  | 0.7027          | 0.8028   |
| 0.1827        | 54.7692 | 178  | 0.6764          | 0.8310   |
| 0.1558        | 56.0    | 182  | 0.7398          | 0.7887   |
| 0.1558        | 56.9231 | 185  | 0.7787          | 0.8169   |
| 0.1558        | 57.8462 | 188  | 0.7678          | 0.8169   |
| 0.1637        | 58.7692 | 191  | 0.7898          | 0.7606   |
| 0.1637        | 60.0    | 195  | 0.7105          | 0.8310   |
| 0.1637        | 60.9231 | 198  | 0.7262          | 0.8592   |
| 0.1591        | 61.8462 | 201  | 0.7464          | 0.8169   |
| 0.1591        | 62.7692 | 204  | 0.7233          | 0.8310   |
| 0.1591        | 64.0    | 208  | 0.7263          | 0.8310   |
| 0.1521        | 64.9231 | 211  | 0.7377          | 0.8028   |
| 0.1521        | 65.8462 | 214  | 0.7267          | 0.8310   |
| 0.1521        | 66.7692 | 217  | 0.7178          | 0.8169   |
| 0.157         | 68.0    | 221  | 0.8585          | 0.7887   |
| 0.157         | 68.9231 | 224  | 0.8629          | 0.7887   |
| 0.157         | 69.8462 | 227  | 0.7329          | 0.8028   |
| 0.1593        | 70.7692 | 230  | 0.6997          | 0.8310   |
| 0.1593        | 72.0    | 234  | 0.8074          | 0.8028   |
| 0.1593        | 72.9231 | 237  | 1.0352          | 0.7887   |
| 0.134         | 73.8462 | 240  | 1.0472          | 0.7887   |
| 0.134         | 74.7692 | 243  | 0.7477          | 0.8169   |
| 0.134         | 76.0    | 247  | 0.7357          | 0.8310   |
| 0.1386        | 76.9231 | 250  | 0.8497          | 0.7887   |
| 0.1386        | 77.8462 | 253  | 0.9464          | 0.7746   |
| 0.1386        | 78.7692 | 256  | 0.8535          | 0.7887   |
| 0.1246        | 80.0    | 260  | 0.7998          | 0.8310   |
| 0.1246        | 80.9231 | 263  | 0.8214          | 0.8310   |
| 0.1246        | 81.8462 | 266  | 0.8374          | 0.8028   |
| 0.1246        | 82.7692 | 269  | 0.8597          | 0.8028   |
| 0.1271        | 84.0    | 273  | 0.8437          | 0.8028   |
| 0.1271        | 84.9231 | 276  | 0.8370          | 0.8028   |
| 0.1271        | 85.8462 | 279  | 0.8298          | 0.8028   |
| 0.1274        | 86.7692 | 282  | 0.8340          | 0.8028   |
| 0.1274        | 88.0    | 286  | 0.8462          | 0.8028   |
| 0.1274        | 88.9231 | 289  | 0.8594          | 0.8028   |
| 0.1251        | 89.8462 | 292  | 0.8504          | 0.8028   |
| 0.1251        | 90.7692 | 295  | 0.8480          | 0.8028   |
| 0.1251        | 92.0    | 299  | 0.8471          | 0.8028   |
| 0.1207        | 92.3077 | 300  | 0.8469          | 0.8028   |


### Framework versions

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

hasta normal