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# beit-base-patch16-224-75-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: 0.2641
- Accuracy: 0.9302

## 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    | 1.0987          | 0.3023   |
| No log        | 2.0   | 4    | 0.6630          | 0.6977   |
| No log        | 3.0   | 6    | 0.8342          | 0.6977   |
| No log        | 4.0   | 8    | 0.6752          | 0.6977   |
| 0.7768        | 5.0   | 10   | 0.5408          | 0.7209   |
| 0.7768        | 6.0   | 12   | 0.7252          | 0.6977   |
| 0.7768        | 7.0   | 14   | 0.5609          | 0.7209   |
| 0.7768        | 8.0   | 16   | 0.7345          | 0.6977   |
| 0.7768        | 9.0   | 18   | 0.4614          | 0.7674   |
| 0.4249        | 10.0  | 20   | 0.4434          | 0.8372   |
| 0.4249        | 11.0  | 22   | 0.7552          | 0.7442   |
| 0.4249        | 12.0  | 24   | 0.4142          | 0.7674   |
| 0.4249        | 13.0  | 26   | 0.7183          | 0.7442   |
| 0.4249        | 14.0  | 28   | 0.5591          | 0.7907   |
| 0.3506        | 15.0  | 30   | 0.4363          | 0.6977   |
| 0.3506        | 16.0  | 32   | 0.5738          | 0.7907   |
| 0.3506        | 17.0  | 34   | 0.4286          | 0.8140   |
| 0.3506        | 18.0  | 36   | 0.4200          | 0.8140   |
| 0.3506        | 19.0  | 38   | 0.6514          | 0.7442   |
| 0.3434        | 20.0  | 40   | 0.4190          | 0.7907   |
| 0.3434        | 21.0  | 42   | 0.6220          | 0.8140   |
| 0.3434        | 22.0  | 44   | 0.6334          | 0.7907   |
| 0.3434        | 23.0  | 46   | 0.4487          | 0.8372   |
| 0.3434        | 24.0  | 48   | 0.4960          | 0.8605   |
| 0.2498        | 25.0  | 50   | 0.4179          | 0.8605   |
| 0.2498        | 26.0  | 52   | 0.3221          | 0.8605   |
| 0.2498        | 27.0  | 54   | 0.4776          | 0.8372   |
| 0.2498        | 28.0  | 56   | 0.5756          | 0.8605   |
| 0.2498        | 29.0  | 58   | 0.5444          | 0.8372   |
| 0.2461        | 30.0  | 60   | 0.3973          | 0.8605   |
| 0.2461        | 31.0  | 62   | 0.3672          | 0.8605   |
| 0.2461        | 32.0  | 64   | 0.4071          | 0.8837   |
| 0.2461        | 33.0  | 66   | 0.4678          | 0.7674   |
| 0.2461        | 34.0  | 68   | 0.2641          | 0.9302   |
| 0.2279        | 35.0  | 70   | 0.5551          | 0.8372   |
| 0.2279        | 36.0  | 72   | 0.2727          | 0.9302   |
| 0.2279        | 37.0  | 74   | 0.3312          | 0.8837   |
| 0.2279        | 38.0  | 76   | 0.7485          | 0.7907   |
| 0.2279        | 39.0  | 78   | 0.6407          | 0.8605   |
| 0.183         | 40.0  | 80   | 0.5420          | 0.8372   |
| 0.183         | 41.0  | 82   | 0.7364          | 0.8605   |
| 0.183         | 42.0  | 84   | 0.4141          | 0.8605   |
| 0.183         | 43.0  | 86   | 0.5461          | 0.7907   |
| 0.183         | 44.0  | 88   | 0.3438          | 0.8605   |
| 0.1658        | 45.0  | 90   | 0.3322          | 0.9302   |
| 0.1658        | 46.0  | 92   | 0.3463          | 0.9302   |
| 0.1658        | 47.0  | 94   | 0.6066          | 0.8605   |
| 0.1658        | 48.0  | 96   | 0.6259          | 0.8605   |
| 0.1658        | 49.0  | 98   | 0.4909          | 0.8372   |
| 0.1555        | 50.0  | 100  | 0.6022          | 0.7907   |
| 0.1555        | 51.0  | 102  | 0.5234          | 0.8372   |
| 0.1555        | 52.0  | 104  | 0.4164          | 0.8837   |
| 0.1555        | 53.0  | 106  | 0.3893          | 0.8605   |
| 0.1555        | 54.0  | 108  | 0.3774          | 0.8837   |
| 0.1487        | 55.0  | 110  | 0.7532          | 0.8372   |
| 0.1487        | 56.0  | 112  | 0.7141          | 0.8605   |
| 0.1487        | 57.0  | 114  | 0.4197          | 0.9070   |
| 0.1487        | 58.0  | 116  | 0.6816          | 0.7442   |
| 0.1487        | 59.0  | 118  | 0.5384          | 0.8140   |
| 0.1349        | 60.0  | 120  | 0.4971          | 0.8605   |
| 0.1349        | 61.0  | 122  | 0.4601          | 0.8837   |
| 0.1349        | 62.0  | 124  | 0.4740          | 0.8372   |
| 0.1349        | 63.0  | 126  | 0.5386          | 0.8140   |
| 0.1349        | 64.0  | 128  | 0.3376          | 0.9070   |
| 0.128         | 65.0  | 130  | 0.3905          | 0.9070   |
| 0.128         | 66.0  | 132  | 0.3841          | 0.9302   |
| 0.128         | 67.0  | 134  | 0.3567          | 0.8605   |
| 0.128         | 68.0  | 136  | 0.3985          | 0.8372   |
| 0.128         | 69.0  | 138  | 0.4165          | 0.8372   |
| 0.0875        | 70.0  | 140  | 0.4346          | 0.8605   |
| 0.0875        | 71.0  | 142  | 0.4497          | 0.8372   |
| 0.0875        | 72.0  | 144  | 0.4353          | 0.8837   |
| 0.0875        | 73.0  | 146  | 0.4276          | 0.8837   |
| 0.0875        | 74.0  | 148  | 0.4010          | 0.8837   |
| 0.0932        | 75.0  | 150  | 0.3958          | 0.9070   |
| 0.0932        | 76.0  | 152  | 0.3604          | 0.9070   |
| 0.0932        | 77.0  | 154  | 0.3427          | 0.8837   |
| 0.0932        | 78.0  | 156  | 0.3417          | 0.8837   |
| 0.0932        | 79.0  | 158  | 0.3438          | 0.9070   |
| 0.0943        | 80.0  | 160  | 0.3756          | 0.9302   |
| 0.0943        | 81.0  | 162  | 0.4077          | 0.9302   |
| 0.0943        | 82.0  | 164  | 0.4129          | 0.9302   |
| 0.0943        | 83.0  | 166  | 0.4304          | 0.9302   |
| 0.0943        | 84.0  | 168  | 0.4156          | 0.9302   |
| 0.0753        | 85.0  | 170  | 0.4088          | 0.9070   |
| 0.0753        | 86.0  | 172  | 0.4090          | 0.8837   |
| 0.0753        | 87.0  | 174  | 0.4076          | 0.9070   |
| 0.0753        | 88.0  | 176  | 0.4273          | 0.9070   |
| 0.0753        | 89.0  | 178  | 0.4367          | 0.9070   |
| 0.0846        | 90.0  | 180  | 0.4490          | 0.9070   |
| 0.0846        | 91.0  | 182  | 0.4448          | 0.8837   |
| 0.0846        | 92.0  | 184  | 0.4406          | 0.8837   |
| 0.0846        | 93.0  | 186  | 0.4393          | 0.8837   |
| 0.0846        | 94.0  | 188  | 0.4370          | 0.8837   |
| 0.0865        | 95.0  | 190  | 0.4330          | 0.8837   |
| 0.0865        | 96.0  | 192  | 0.4293          | 0.8837   |
| 0.0865        | 97.0  | 194  | 0.4240          | 0.8837   |
| 0.0865        | 98.0  | 196  | 0.4177          | 0.8837   |
| 0.0865        | 99.0  | 198  | 0.4144          | 0.8837   |
| 0.1019        | 100.0 | 200  | 0.4135          | 0.8837   |


### Framework versions

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

hasta normal