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# beit-base-patch16-224-65-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: 0.3734
- Accuracy: 0.9014

## 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    | 0.6830          | 0.5493   |
| No log        | 1.8462  | 6    | 0.6407          | 0.6479   |
| No log        | 2.7692  | 9    | 0.6612          | 0.5352   |
| 0.7094        | 4.0     | 13   | 0.6175          | 0.6901   |
| 0.7094        | 4.9231  | 16   | 0.5915          | 0.6761   |
| 0.7094        | 5.8462  | 19   | 0.5677          | 0.7042   |
| 0.6444        | 6.7692  | 22   | 0.5177          | 0.7746   |
| 0.6444        | 8.0     | 26   | 0.4928          | 0.7887   |
| 0.6444        | 8.9231  | 29   | 0.5641          | 0.6901   |
| 0.5574        | 9.8462  | 32   | 0.4372          | 0.8169   |
| 0.5574        | 10.7692 | 35   | 0.5677          | 0.7606   |
| 0.5574        | 12.0    | 39   | 0.5032          | 0.7606   |
| 0.543         | 12.9231 | 42   | 0.4745          | 0.8028   |
| 0.543         | 13.8462 | 45   | 0.4057          | 0.8310   |
| 0.543         | 14.7692 | 48   | 0.4013          | 0.7746   |
| 0.4499        | 16.0    | 52   | 0.3670          | 0.8310   |
| 0.4499        | 16.9231 | 55   | 0.4215          | 0.8592   |
| 0.4499        | 17.8462 | 58   | 0.4862          | 0.7746   |
| 0.3902        | 18.7692 | 61   | 0.5781          | 0.7324   |
| 0.3902        | 20.0    | 65   | 0.5249          | 0.8028   |
| 0.3902        | 20.9231 | 68   | 0.3937          | 0.8732   |
| 0.4029        | 21.8462 | 71   | 0.4132          | 0.8451   |
| 0.4029        | 22.7692 | 74   | 0.4178          | 0.8451   |
| 0.4029        | 24.0    | 78   | 0.7273          | 0.7183   |
| 0.3163        | 24.9231 | 81   | 0.4221          | 0.8451   |
| 0.3163        | 25.8462 | 84   | 0.4086          | 0.8732   |
| 0.3163        | 26.7692 | 87   | 0.3946          | 0.8732   |
| 0.2786        | 28.0    | 91   | 0.5320          | 0.8028   |
| 0.2786        | 28.9231 | 94   | 0.4132          | 0.8451   |
| 0.2786        | 29.8462 | 97   | 0.5542          | 0.7746   |
| 0.2763        | 30.7692 | 100  | 0.3734          | 0.9014   |
| 0.2763        | 32.0    | 104  | 0.4479          | 0.8310   |
| 0.2763        | 32.9231 | 107  | 0.3482          | 0.8592   |
| 0.25          | 33.8462 | 110  | 0.5442          | 0.7887   |
| 0.25          | 34.7692 | 113  | 0.4211          | 0.8732   |
| 0.25          | 36.0    | 117  | 0.4860          | 0.8592   |
| 0.2125        | 36.9231 | 120  | 0.4654          | 0.8451   |
| 0.2125        | 37.8462 | 123  | 0.4779          | 0.8592   |
| 0.2125        | 38.7692 | 126  | 0.5692          | 0.8310   |
| 0.2225        | 40.0    | 130  | 0.4912          | 0.8451   |
| 0.2225        | 40.9231 | 133  | 0.4528          | 0.8310   |
| 0.2225        | 41.8462 | 136  | 0.4470          | 0.7887   |
| 0.2225        | 42.7692 | 139  | 0.4251          | 0.8028   |
| 0.1991        | 44.0    | 143  | 0.4864          | 0.8028   |
| 0.1991        | 44.9231 | 146  | 0.4652          | 0.8592   |
| 0.1991        | 45.8462 | 149  | 0.5949          | 0.8310   |
| 0.164         | 46.7692 | 152  | 1.0035          | 0.7465   |
| 0.164         | 48.0    | 156  | 0.6393          | 0.8310   |
| 0.164         | 48.9231 | 159  | 0.9222          | 0.6761   |
| 0.1974        | 49.8462 | 162  | 1.0633          | 0.6901   |
| 0.1974        | 50.7692 | 165  | 0.6050          | 0.8310   |
| 0.1974        | 52.0    | 169  | 0.7134          | 0.8310   |
| 0.213         | 52.9231 | 172  | 0.6649          | 0.8310   |
| 0.213         | 53.8462 | 175  | 0.7126          | 0.8310   |
| 0.213         | 54.7692 | 178  | 0.6906          | 0.8169   |
| 0.1642        | 56.0    | 182  | 0.6956          | 0.8310   |
| 0.1642        | 56.9231 | 185  | 0.5828          | 0.8028   |
| 0.1642        | 57.8462 | 188  | 0.5866          | 0.8028   |
| 0.1657        | 58.7692 | 191  | 0.6172          | 0.8169   |
| 0.1657        | 60.0    | 195  | 0.7428          | 0.8310   |
| 0.1657        | 60.9231 | 198  | 0.8981          | 0.8169   |
| 0.1347        | 61.8462 | 201  | 0.7168          | 0.8169   |
| 0.1347        | 62.7692 | 204  | 0.8026          | 0.8451   |
| 0.1347        | 64.0    | 208  | 0.8639          | 0.8451   |
| 0.1335        | 64.9231 | 211  | 0.7604          | 0.8169   |
| 0.1335        | 65.8462 | 214  | 0.7993          | 0.8169   |
| 0.1335        | 66.7692 | 217  | 0.8330          | 0.8592   |
| 0.145         | 68.0    | 221  | 0.8143          | 0.8592   |
| 0.145         | 68.9231 | 224  | 0.7520          | 0.8592   |
| 0.145         | 69.8462 | 227  | 0.7216          | 0.8451   |
| 0.1658        | 70.7692 | 230  | 0.7968          | 0.8592   |
| 0.1658        | 72.0    | 234  | 0.7730          | 0.8732   |
| 0.1658        | 72.9231 | 237  | 0.7450          | 0.8732   |
| 0.1381        | 73.8462 | 240  | 0.7855          | 0.8732   |
| 0.1381        | 74.7692 | 243  | 0.8253          | 0.8592   |
| 0.1381        | 76.0    | 247  | 0.8065          | 0.8592   |
| 0.1306        | 76.9231 | 250  | 0.7778          | 0.8592   |
| 0.1306        | 77.8462 | 253  | 0.7814          | 0.8592   |
| 0.1306        | 78.7692 | 256  | 0.7335          | 0.8310   |
| 0.1027        | 80.0    | 260  | 0.7372          | 0.8451   |
| 0.1027        | 80.9231 | 263  | 0.7618          | 0.8451   |
| 0.1027        | 81.8462 | 266  | 0.7891          | 0.8451   |
| 0.1027        | 82.7692 | 269  | 0.8287          | 0.8592   |
| 0.1296        | 84.0    | 273  | 0.8412          | 0.8451   |
| 0.1296        | 84.9231 | 276  | 0.8014          | 0.8451   |
| 0.1296        | 85.8462 | 279  | 0.7530          | 0.8451   |
| 0.1162        | 86.7692 | 282  | 0.7243          | 0.8451   |
| 0.1162        | 88.0    | 286  | 0.7247          | 0.8451   |
| 0.1162        | 88.9231 | 289  | 0.7354          | 0.8451   |
| 0.1166        | 89.8462 | 292  | 0.7391          | 0.8592   |
| 0.1166        | 90.7692 | 295  | 0.7390          | 0.8592   |
| 0.1166        | 92.0    | 299  | 0.7374          | 0.8592   |
| 0.1031        | 92.3077 | 300  | 0.7373          | 0.8592   |


### Framework versions

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

hasta normal