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# beit-base-patch16-224-65-fold1

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: 0.3454
- 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    | 0.7288          | 0.4648   |
| No log        | 1.8462  | 6    | 0.6816          | 0.5634   |
| No log        | 2.7692  | 9    | 0.6402          | 0.6479   |
| 0.699         | 4.0     | 13   | 0.6028          | 0.6620   |
| 0.699         | 4.9231  | 16   | 0.5877          | 0.6620   |
| 0.699         | 5.8462  | 19   | 0.5858          | 0.6620   |
| 0.618         | 6.7692  | 22   | 0.5099          | 0.7324   |
| 0.618         | 8.0     | 26   | 0.5926          | 0.6901   |
| 0.618         | 8.9231  | 29   | 0.4867          | 0.7746   |
| 0.5564        | 9.8462  | 32   | 0.4736          | 0.7746   |
| 0.5564        | 10.7692 | 35   | 0.4780          | 0.7606   |
| 0.5564        | 12.0    | 39   | 0.4812          | 0.7606   |
| 0.5017        | 12.9231 | 42   | 0.4834          | 0.7887   |
| 0.5017        | 13.8462 | 45   | 0.4662          | 0.8451   |
| 0.5017        | 14.7692 | 48   | 0.4560          | 0.8028   |
| 0.4603        | 16.0    | 52   | 0.4046          | 0.8169   |
| 0.4603        | 16.9231 | 55   | 0.3368          | 0.8451   |
| 0.4603        | 17.8462 | 58   | 0.3454          | 0.8732   |
| 0.4037        | 18.7692 | 61   | 0.6355          | 0.7324   |
| 0.4037        | 20.0    | 65   | 0.3624          | 0.8592   |
| 0.4037        | 20.9231 | 68   | 0.3748          | 0.8592   |
| 0.3696        | 21.8462 | 71   | 0.3799          | 0.8451   |
| 0.3696        | 22.7692 | 74   | 0.3886          | 0.8592   |
| 0.3696        | 24.0    | 78   | 0.3485          | 0.8451   |
| 0.3193        | 24.9231 | 81   | 0.3385          | 0.8732   |
| 0.3193        | 25.8462 | 84   | 0.3691          | 0.8592   |
| 0.3193        | 26.7692 | 87   | 0.3863          | 0.8732   |
| 0.308         | 28.0    | 91   | 0.3722          | 0.8732   |
| 0.308         | 28.9231 | 94   | 0.3481          | 0.8732   |
| 0.308         | 29.8462 | 97   | 0.3488          | 0.8732   |
| 0.2306        | 30.7692 | 100  | 0.4253          | 0.8451   |
| 0.2306        | 32.0    | 104  | 0.6244          | 0.7887   |
| 0.2306        | 32.9231 | 107  | 0.4688          | 0.7887   |
| 0.2431        | 33.8462 | 110  | 0.6080          | 0.7746   |
| 0.2431        | 34.7692 | 113  | 0.5795          | 0.7606   |
| 0.2431        | 36.0    | 117  | 0.5478          | 0.7887   |
| 0.2174        | 36.9231 | 120  | 0.5283          | 0.8169   |
| 0.2174        | 37.8462 | 123  | 0.5356          | 0.7887   |
| 0.2174        | 38.7692 | 126  | 0.5723          | 0.8169   |
| 0.1928        | 40.0    | 130  | 0.5489          | 0.8028   |
| 0.1928        | 40.9231 | 133  | 0.5277          | 0.7887   |
| 0.1928        | 41.8462 | 136  | 0.4725          | 0.8028   |
| 0.1928        | 42.7692 | 139  | 0.7954          | 0.7606   |
| 0.1919        | 44.0    | 143  | 0.5396          | 0.7887   |
| 0.1919        | 44.9231 | 146  | 0.6012          | 0.7746   |
| 0.1919        | 45.8462 | 149  | 0.6192          | 0.8028   |
| 0.1886        | 46.7692 | 152  | 0.6233          | 0.8028   |
| 0.1886        | 48.0    | 156  | 0.6465          | 0.8169   |
| 0.1886        | 48.9231 | 159  | 0.7676          | 0.8028   |
| 0.1661        | 49.8462 | 162  | 0.5266          | 0.8028   |
| 0.1661        | 50.7692 | 165  | 0.5127          | 0.8310   |
| 0.1661        | 52.0    | 169  | 0.5554          | 0.8169   |
| 0.1746        | 52.9231 | 172  | 0.6333          | 0.8310   |
| 0.1746        | 53.8462 | 175  | 0.6069          | 0.7887   |
| 0.1746        | 54.7692 | 178  | 0.6963          | 0.7887   |
| 0.1487        | 56.0    | 182  | 0.7242          | 0.8028   |
| 0.1487        | 56.9231 | 185  | 0.8501          | 0.7887   |
| 0.1487        | 57.8462 | 188  | 0.6207          | 0.7887   |
| 0.1785        | 58.7692 | 191  | 0.5805          | 0.7887   |
| 0.1785        | 60.0    | 195  | 0.5998          | 0.8169   |
| 0.1785        | 60.9231 | 198  | 0.5254          | 0.7887   |
| 0.1711        | 61.8462 | 201  | 0.5723          | 0.8028   |
| 0.1711        | 62.7692 | 204  | 0.7298          | 0.7887   |
| 0.1711        | 64.0    | 208  | 0.6647          | 0.7887   |
| 0.1637        | 64.9231 | 211  | 0.7011          | 0.8310   |
| 0.1637        | 65.8462 | 214  | 0.7031          | 0.8169   |
| 0.1637        | 66.7692 | 217  | 0.7163          | 0.8028   |
| 0.1618        | 68.0    | 221  | 0.6511          | 0.8169   |
| 0.1618        | 68.9231 | 224  | 0.6291          | 0.8310   |
| 0.1618        | 69.8462 | 227  | 0.6044          | 0.8451   |
| 0.153         | 70.7692 | 230  | 0.5888          | 0.8310   |
| 0.153         | 72.0    | 234  | 0.5881          | 0.8310   |
| 0.153         | 72.9231 | 237  | 0.5604          | 0.8169   |
| 0.1365        | 73.8462 | 240  | 0.6055          | 0.8310   |
| 0.1365        | 74.7692 | 243  | 0.6326          | 0.8028   |
| 0.1365        | 76.0    | 247  | 0.6686          | 0.7746   |
| 0.1231        | 76.9231 | 250  | 0.6955          | 0.7746   |
| 0.1231        | 77.8462 | 253  | 0.7302          | 0.7746   |
| 0.1231        | 78.7692 | 256  | 0.7928          | 0.7746   |
| 0.132         | 80.0    | 260  | 0.7247          | 0.7887   |
| 0.132         | 80.9231 | 263  | 0.7243          | 0.8028   |
| 0.132         | 81.8462 | 266  | 0.7361          | 0.8169   |
| 0.132         | 82.7692 | 269  | 0.7179          | 0.7746   |
| 0.1126        | 84.0    | 273  | 0.7054          | 0.7746   |
| 0.1126        | 84.9231 | 276  | 0.7192          | 0.7887   |
| 0.1126        | 85.8462 | 279  | 0.7358          | 0.7746   |
| 0.1141        | 86.7692 | 282  | 0.7575          | 0.7887   |
| 0.1141        | 88.0    | 286  | 0.7741          | 0.7887   |
| 0.1141        | 88.9231 | 289  | 0.7878          | 0.7887   |
| 0.1105        | 89.8462 | 292  | 0.7857          | 0.7887   |
| 0.1105        | 90.7692 | 295  | 0.7814          | 0.7887   |
| 0.1105        | 92.0    | 299  | 0.7785          | 0.7887   |
| 0.1006        | 92.3077 | 300  | 0.7782          | 0.7887   |


### Framework versions

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

hasta normal