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# smids_3x_beit_base_rms_00001_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: 0.9909
- Accuracy: 0.9002

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2362        | 1.0   | 225   | 0.2699          | 0.9035   |
| 0.1679        | 2.0   | 450   | 0.2716          | 0.9101   |
| 0.0726        | 3.0   | 675   | 0.3178          | 0.8968   |
| 0.0695        | 4.0   | 900   | 0.3843          | 0.9035   |
| 0.0417        | 5.0   | 1125  | 0.4680          | 0.8835   |
| 0.0473        | 6.0   | 1350  | 0.5055          | 0.9002   |
| 0.0543        | 7.0   | 1575  | 0.5652          | 0.8935   |
| 0.0498        | 8.0   | 1800  | 0.5730          | 0.9018   |
| 0.0051        | 9.0   | 2025  | 0.7436          | 0.8985   |
| 0.0809        | 10.0  | 2250  | 0.7929          | 0.8968   |
| 0.0184        | 11.0  | 2475  | 0.8279          | 0.8702   |
| 0.0005        | 12.0  | 2700  | 0.7228          | 0.8985   |
| 0.0232        | 13.0  | 2925  | 0.7729          | 0.8869   |
| 0.0419        | 14.0  | 3150  | 0.7425          | 0.8985   |
| 0.0106        | 15.0  | 3375  | 0.7246          | 0.8952   |
| 0.0338        | 16.0  | 3600  | 0.7871          | 0.8885   |
| 0.0349        | 17.0  | 3825  | 0.8649          | 0.9002   |
| 0.0092        | 18.0  | 4050  | 0.7633          | 0.8902   |
| 0.0509        | 19.0  | 4275  | 0.8796          | 0.8885   |
| 0.0566        | 20.0  | 4500  | 0.8230          | 0.9068   |
| 0.0001        | 21.0  | 4725  | 0.8115          | 0.8968   |
| 0.0178        | 22.0  | 4950  | 0.8547          | 0.9118   |
| 0.0024        | 23.0  | 5175  | 0.8065          | 0.9135   |
| 0.0           | 24.0  | 5400  | 0.8301          | 0.9085   |
| 0.0494        | 25.0  | 5625  | 0.9352          | 0.9035   |
| 0.0           | 26.0  | 5850  | 0.9033          | 0.8902   |
| 0.0001        | 27.0  | 6075  | 0.8768          | 0.8918   |
| 0.0           | 28.0  | 6300  | 0.8873          | 0.9002   |
| 0.0           | 29.0  | 6525  | 0.8635          | 0.9018   |
| 0.007         | 30.0  | 6750  | 0.8770          | 0.8952   |
| 0.0055        | 31.0  | 6975  | 0.9657          | 0.8985   |
| 0.0           | 32.0  | 7200  | 0.9004          | 0.8935   |
| 0.0016        | 33.0  | 7425  | 0.9326          | 0.8918   |
| 0.003         | 34.0  | 7650  | 0.9751          | 0.9002   |
| 0.0           | 35.0  | 7875  | 0.9491          | 0.9052   |
| 0.0043        | 36.0  | 8100  | 0.9618          | 0.8952   |
| 0.0087        | 37.0  | 8325  | 0.9634          | 0.8902   |
| 0.0294        | 38.0  | 8550  | 1.0166          | 0.8952   |
| 0.0044        | 39.0  | 8775  | 0.9519          | 0.8968   |
| 0.0           | 40.0  | 9000  | 0.9467          | 0.8985   |
| 0.0001        | 41.0  | 9225  | 0.9520          | 0.9002   |
| 0.0002        | 42.0  | 9450  | 0.9492          | 0.9002   |
| 0.0           | 43.0  | 9675  | 0.9742          | 0.8985   |
| 0.0           | 44.0  | 9900  | 1.0125          | 0.9018   |
| 0.0           | 45.0  | 10125 | 0.9921          | 0.9002   |
| 0.0           | 46.0  | 10350 | 0.9848          | 0.9002   |
| 0.0           | 47.0  | 10575 | 0.9786          | 0.8985   |
| 0.0           | 48.0  | 10800 | 0.9903          | 0.9002   |
| 0.0039        | 49.0  | 11025 | 0.9883          | 0.9002   |
| 0.0003        | 50.0  | 11250 | 0.9909          | 0.9002   |


### 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