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# smids_3x_beit_base_rms_00001_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: 0.8235
- Accuracy: 0.8998

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3449        | 1.0   | 226   | 0.2404          | 0.8982   |
| 0.164         | 2.0   | 452   | 0.2594          | 0.9098   |
| 0.0958        | 3.0   | 678   | 0.3903          | 0.8715   |
| 0.1173        | 4.0   | 904   | 0.3785          | 0.8982   |
| 0.071         | 5.0   | 1130  | 0.3702          | 0.9032   |
| 0.0146        | 6.0   | 1356  | 0.5160          | 0.8932   |
| 0.004         | 7.0   | 1582  | 0.5036          | 0.8965   |
| 0.0021        | 8.0   | 1808  | 0.5998          | 0.9015   |
| 0.0217        | 9.0   | 2034  | 0.6221          | 0.8998   |
| 0.0119        | 10.0  | 2260  | 0.6511          | 0.9082   |
| 0.0029        | 11.0  | 2486  | 0.6550          | 0.8932   |
| 0.0209        | 12.0  | 2712  | 0.5564          | 0.9082   |
| 0.0119        | 13.0  | 2938  | 0.7071          | 0.9015   |
| 0.0109        | 14.0  | 3164  | 0.6721          | 0.8965   |
| 0.0179        | 15.0  | 3390  | 0.6523          | 0.8965   |
| 0.0016        | 16.0  | 3616  | 0.6369          | 0.9149   |
| 0.0197        | 17.0  | 3842  | 0.8098          | 0.8932   |
| 0.0022        | 18.0  | 4068  | 0.7112          | 0.8948   |
| 0.017         | 19.0  | 4294  | 0.8580          | 0.8898   |
| 0.0002        | 20.0  | 4520  | 0.8600          | 0.8915   |
| 0.014         | 21.0  | 4746  | 0.8484          | 0.8932   |
| 0.0155        | 22.0  | 4972  | 0.7756          | 0.8932   |
| 0.0055        | 23.0  | 5198  | 0.7307          | 0.9082   |
| 0.016         | 24.0  | 5424  | 0.7520          | 0.9065   |
| 0.0           | 25.0  | 5650  | 0.6900          | 0.9165   |
| 0.0009        | 26.0  | 5876  | 0.7482          | 0.8998   |
| 0.0           | 27.0  | 6102  | 0.6921          | 0.9032   |
| 0.0022        | 28.0  | 6328  | 0.6800          | 0.9098   |
| 0.0           | 29.0  | 6554  | 0.6295          | 0.9215   |
| 0.0004        | 30.0  | 6780  | 0.6201          | 0.9182   |
| 0.0           | 31.0  | 7006  | 0.6546          | 0.9182   |
| 0.0           | 32.0  | 7232  | 0.6675          | 0.9098   |
| 0.0055        | 33.0  | 7458  | 0.7721          | 0.9048   |
| 0.0014        | 34.0  | 7684  | 0.8129          | 0.8965   |
| 0.0           | 35.0  | 7910  | 0.8045          | 0.8998   |
| 0.0           | 36.0  | 8136  | 0.7737          | 0.8998   |
| 0.0005        | 37.0  | 8362  | 0.7575          | 0.9065   |
| 0.0           | 38.0  | 8588  | 0.7935          | 0.9098   |
| 0.0001        | 39.0  | 8814  | 0.8075          | 0.8948   |
| 0.0           | 40.0  | 9040  | 0.7870          | 0.9065   |
| 0.0033        | 41.0  | 9266  | 0.7830          | 0.8965   |
| 0.0059        | 42.0  | 9492  | 0.8352          | 0.9015   |
| 0.0           | 43.0  | 9718  | 0.7765          | 0.9098   |
| 0.0           | 44.0  | 9944  | 0.8178          | 0.9065   |
| 0.0123        | 45.0  | 10170 | 0.8336          | 0.8982   |
| 0.0           | 46.0  | 10396 | 0.8075          | 0.9048   |
| 0.0           | 47.0  | 10622 | 0.8113          | 0.9048   |
| 0.0           | 48.0  | 10848 | 0.8125          | 0.9032   |
| 0.0           | 49.0  | 11074 | 0.8258          | 0.8982   |
| 0.0           | 50.0  | 11300 | 0.8235          | 0.8998   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm