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# beit-base-patch16-224-75-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: 0.2509
- 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    | 0.5130          | 0.7907   |
| No log        | 2.0   | 4    | 0.4861          | 0.7907   |
| No log        | 3.0   | 6    | 0.4775          | 0.7907   |
| No log        | 4.0   | 8    | 0.4419          | 0.7907   |
| 0.4909        | 5.0   | 10   | 0.3672          | 0.8605   |
| 0.4909        | 6.0   | 12   | 0.3301          | 0.8837   |
| 0.4909        | 7.0   | 14   | 0.3131          | 0.8837   |
| 0.4909        | 8.0   | 16   | 0.4535          | 0.8605   |
| 0.4909        | 9.0   | 18   | 0.3088          | 0.8372   |
| 0.3473        | 10.0  | 20   | 0.4453          | 0.8837   |
| 0.3473        | 11.0  | 22   | 0.4234          | 0.8605   |
| 0.3473        | 12.0  | 24   | 0.3601          | 0.8837   |
| 0.3473        | 13.0  | 26   | 0.3658          | 0.9070   |
| 0.3473        | 14.0  | 28   | 0.3081          | 0.8837   |
| 0.2903        | 15.0  | 30   | 0.4128          | 0.8837   |
| 0.2903        | 16.0  | 32   | 0.2555          | 0.8605   |
| 0.2903        | 17.0  | 34   | 0.3341          | 0.8837   |
| 0.2903        | 18.0  | 36   | 0.2427          | 0.8837   |
| 0.2903        | 19.0  | 38   | 0.4325          | 0.8372   |
| 0.2673        | 20.0  | 40   | 0.2637          | 0.9070   |
| 0.2673        | 21.0  | 42   | 0.2919          | 0.8837   |
| 0.2673        | 22.0  | 44   | 0.3139          | 0.8837   |
| 0.2673        | 23.0  | 46   | 0.2411          | 0.8837   |
| 0.2673        | 24.0  | 48   | 0.4645          | 0.9070   |
| 0.2103        | 25.0  | 50   | 0.5084          | 0.8605   |
| 0.2103        | 26.0  | 52   | 0.2308          | 0.9070   |
| 0.2103        | 27.0  | 54   | 0.3450          | 0.8605   |
| 0.2103        | 28.0  | 56   | 0.3444          | 0.8605   |
| 0.2103        | 29.0  | 58   | 0.2546          | 0.9070   |
| 0.1673        | 30.0  | 60   | 0.9117          | 0.8140   |
| 0.1673        | 31.0  | 62   | 0.8437          | 0.8140   |
| 0.1673        | 32.0  | 64   | 0.6758          | 0.8372   |
| 0.1673        | 33.0  | 66   | 0.8019          | 0.8140   |
| 0.1673        | 34.0  | 68   | 0.3364          | 0.8837   |
| 0.1677        | 35.0  | 70   | 0.2928          | 0.8837   |
| 0.1677        | 36.0  | 72   | 0.2547          | 0.9070   |
| 0.1677        | 37.0  | 74   | 0.2969          | 0.8837   |
| 0.1677        | 38.0  | 76   | 0.5706          | 0.8837   |
| 0.1677        | 39.0  | 78   | 0.7006          | 0.8837   |
| 0.1407        | 40.0  | 80   | 0.4321          | 0.8837   |
| 0.1407        | 41.0  | 82   | 0.4366          | 0.8837   |
| 0.1407        | 42.0  | 84   | 0.3956          | 0.8837   |
| 0.1407        | 43.0  | 86   | 0.2290          | 0.8372   |
| 0.1407        | 44.0  | 88   | 0.3665          | 0.8837   |
| 0.1474        | 45.0  | 90   | 0.4465          | 0.8605   |
| 0.1474        | 46.0  | 92   | 0.7279          | 0.8605   |
| 0.1474        | 47.0  | 94   | 0.5259          | 0.8605   |
| 0.1474        | 48.0  | 96   | 0.5832          | 0.8837   |
| 0.1474        | 49.0  | 98   | 0.7328          | 0.8837   |
| 0.1344        | 50.0  | 100  | 0.3890          | 0.8837   |
| 0.1344        | 51.0  | 102  | 0.2642          | 0.8837   |
| 0.1344        | 52.0  | 104  | 0.3710          | 0.9070   |
| 0.1344        | 53.0  | 106  | 0.4773          | 0.9070   |
| 0.1344        | 54.0  | 108  | 0.3628          | 0.9302   |
| 0.1166        | 55.0  | 110  | 0.4389          | 0.9070   |
| 0.1166        | 56.0  | 112  | 0.4813          | 0.9070   |
| 0.1166        | 57.0  | 114  | 0.5328          | 0.9070   |
| 0.1166        | 58.0  | 116  | 0.5342          | 0.9070   |
| 0.1166        | 59.0  | 118  | 0.4892          | 0.9070   |
| 0.097         | 60.0  | 120  | 0.5857          | 0.9070   |
| 0.097         | 61.0  | 122  | 0.6681          | 0.9070   |
| 0.097         | 62.0  | 124  | 0.5947          | 0.9070   |
| 0.097         | 63.0  | 126  | 0.4749          | 0.9070   |
| 0.097         | 64.0  | 128  | 0.6091          | 0.8837   |
| 0.1076        | 65.0  | 130  | 0.9725          | 0.8605   |
| 0.1076        | 66.0  | 132  | 1.1372          | 0.8140   |
| 0.1076        | 67.0  | 134  | 0.7109          | 0.8605   |
| 0.1076        | 68.0  | 136  | 0.3549          | 0.9302   |
| 0.1076        | 69.0  | 138  | 0.2709          | 0.9302   |
| 0.0914        | 70.0  | 140  | 0.3316          | 0.9302   |
| 0.0914        | 71.0  | 142  | 0.3176          | 0.9302   |
| 0.0914        | 72.0  | 144  | 0.2509          | 0.9535   |
| 0.0914        | 73.0  | 146  | 0.2256          | 0.9070   |
| 0.0914        | 74.0  | 148  | 0.2570          | 0.9070   |
| 0.0815        | 75.0  | 150  | 0.3081          | 0.9535   |
| 0.0815        | 76.0  | 152  | 0.4199          | 0.9302   |
| 0.0815        | 77.0  | 154  | 0.4324          | 0.9302   |
| 0.0815        | 78.0  | 156  | 0.3928          | 0.9302   |
| 0.0815        | 79.0  | 158  | 0.3700          | 0.9302   |
| 0.0878        | 80.0  | 160  | 0.3812          | 0.9302   |
| 0.0878        | 81.0  | 162  | 0.4300          | 0.9302   |
| 0.0878        | 82.0  | 164  | 0.4289          | 0.9302   |
| 0.0878        | 83.0  | 166  | 0.4125          | 0.9302   |
| 0.0878        | 84.0  | 168  | 0.4351          | 0.9302   |
| 0.0725        | 85.0  | 170  | 0.5046          | 0.9302   |
| 0.0725        | 86.0  | 172  | 0.5692          | 0.9070   |
| 0.0725        | 87.0  | 174  | 0.5486          | 0.9070   |
| 0.0725        | 88.0  | 176  | 0.5310          | 0.9302   |
| 0.0725        | 89.0  | 178  | 0.4662          | 0.9302   |
| 0.0944        | 90.0  | 180  | 0.4070          | 0.9302   |
| 0.0944        | 91.0  | 182  | 0.3768          | 0.9302   |
| 0.0944        | 92.0  | 184  | 0.3884          | 0.9302   |
| 0.0944        | 93.0  | 186  | 0.3851          | 0.9302   |
| 0.0944        | 94.0  | 188  | 0.3759          | 0.9302   |
| 0.0739        | 95.0  | 190  | 0.3608          | 0.9302   |
| 0.0739        | 96.0  | 192  | 0.3456          | 0.9302   |
| 0.0739        | 97.0  | 194  | 0.3360          | 0.9302   |
| 0.0739        | 98.0  | 196  | 0.3312          | 0.9302   |
| 0.0739        | 99.0  | 198  | 0.3321          | 0.9302   |
| 0.0612        | 100.0 | 200  | 0.3331          | 0.9302   |


### Framework versions

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

hasta normal