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# smids_3x_beit_base_rms_0001_fold3

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: 1.6713
- Accuracy: 0.855

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7637        | 1.0   | 225   | 0.7530          | 0.7217   |
| 0.5551        | 2.0   | 450   | 0.6161          | 0.7583   |
| 0.4831        | 3.0   | 675   | 0.4948          | 0.7833   |
| 0.3281        | 4.0   | 900   | 0.5414          | 0.8033   |
| 0.3506        | 5.0   | 1125  | 0.4226          | 0.815    |
| 0.3328        | 6.0   | 1350  | 0.4220          | 0.83     |
| 0.2581        | 7.0   | 1575  | 0.5786          | 0.7883   |
| 0.1949        | 8.0   | 1800  | 0.5329          | 0.8133   |
| 0.2071        | 9.0   | 2025  | 0.4652          | 0.8417   |
| 0.1906        | 10.0  | 2250  | 0.5303          | 0.82     |
| 0.1705        | 11.0  | 2475  | 0.6288          | 0.8283   |
| 0.153         | 12.0  | 2700  | 0.5236          | 0.8383   |
| 0.0688        | 13.0  | 2925  | 0.7459          | 0.8133   |
| 0.0899        | 14.0  | 3150  | 0.8275          | 0.8117   |
| 0.0904        | 15.0  | 3375  | 0.7966          | 0.8467   |
| 0.0822        | 16.0  | 3600  | 0.8714          | 0.835    |
| 0.112         | 17.0  | 3825  | 0.8906          | 0.8433   |
| 0.0635        | 18.0  | 4050  | 0.8797          | 0.835    |
| 0.0639        | 19.0  | 4275  | 0.8962          | 0.85     |
| 0.0335        | 20.0  | 4500  | 1.1500          | 0.815    |
| 0.0798        | 21.0  | 4725  | 0.9654          | 0.82     |
| 0.0508        | 22.0  | 4950  | 1.1138          | 0.8467   |
| 0.0254        | 23.0  | 5175  | 0.9088          | 0.8367   |
| 0.0398        | 24.0  | 5400  | 1.1000          | 0.83     |
| 0.083         | 25.0  | 5625  | 0.9695          | 0.8367   |
| 0.0354        | 26.0  | 5850  | 1.1614          | 0.8317   |
| 0.018         | 27.0  | 6075  | 1.1091          | 0.8533   |
| 0.0554        | 28.0  | 6300  | 1.0860          | 0.8417   |
| 0.0435        | 29.0  | 6525  | 1.0082          | 0.8533   |
| 0.0521        | 30.0  | 6750  | 1.0251          | 0.8333   |
| 0.0523        | 31.0  | 6975  | 1.1028          | 0.8267   |
| 0.0058        | 32.0  | 7200  | 1.2099          | 0.8433   |
| 0.0089        | 33.0  | 7425  | 1.4585          | 0.8383   |
| 0.0197        | 34.0  | 7650  | 1.2388          | 0.8483   |
| 0.0029        | 35.0  | 7875  | 1.3364          | 0.83     |
| 0.0016        | 36.0  | 8100  | 1.4458          | 0.8417   |
| 0.0092        | 37.0  | 8325  | 1.4004          | 0.835    |
| 0.0003        | 38.0  | 8550  | 1.4317          | 0.8417   |
| 0.0011        | 39.0  | 8775  | 1.2820          | 0.8417   |
| 0.0089        | 40.0  | 9000  | 1.5154          | 0.8417   |
| 0.0236        | 41.0  | 9225  | 1.3755          | 0.8467   |
| 0.0009        | 42.0  | 9450  | 1.6899          | 0.8517   |
| 0.0128        | 43.0  | 9675  | 1.5784          | 0.845    |
| 0.0006        | 44.0  | 9900  | 1.6022          | 0.8517   |
| 0.0002        | 45.0  | 10125 | 1.4557          | 0.8467   |
| 0.0206        | 46.0  | 10350 | 1.5017          | 0.855    |
| 0.006         | 47.0  | 10575 | 1.5387          | 0.855    |
| 0.0004        | 48.0  | 10800 | 1.6762          | 0.855    |
| 0.001         | 49.0  | 11025 | 1.7088          | 0.855    |
| 0.0003        | 50.0  | 11250 | 1.6713          | 0.855    |


### 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