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# smids_3x_beit_base_adamax_001_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: 1.5511
- Accuracy: 0.8533

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4815        | 1.0   | 225   | 0.6186          | 0.7683   |
| 0.3544        | 2.0   | 450   | 0.5568          | 0.81     |
| 0.3541        | 3.0   | 675   | 0.4838          | 0.8283   |
| 0.3428        | 4.0   | 900   | 0.5170          | 0.8317   |
| 0.2833        | 5.0   | 1125  | 0.5822          | 0.7917   |
| 0.2012        | 6.0   | 1350  | 0.4801          | 0.8333   |
| 0.1747        | 7.0   | 1575  | 0.5354          | 0.8283   |
| 0.244         | 8.0   | 1800  | 0.5484          | 0.8467   |
| 0.2186        | 9.0   | 2025  | 0.6193          | 0.8167   |
| 0.155         | 10.0  | 2250  | 0.5230          | 0.8483   |
| 0.088         | 11.0  | 2475  | 0.6574          | 0.8367   |
| 0.1048        | 12.0  | 2700  | 0.5983          | 0.8517   |
| 0.0758        | 13.0  | 2925  | 0.7255          | 0.835    |
| 0.0236        | 14.0  | 3150  | 0.7448          | 0.835    |
| 0.088         | 15.0  | 3375  | 0.9152          | 0.84     |
| 0.0685        | 16.0  | 3600  | 0.9354          | 0.8167   |
| 0.0326        | 17.0  | 3825  | 0.9022          | 0.8417   |
| 0.0167        | 18.0  | 4050  | 0.9397          | 0.8417   |
| 0.0222        | 19.0  | 4275  | 0.9135          | 0.8433   |
| 0.0409        | 20.0  | 4500  | 1.1656          | 0.8467   |
| 0.0192        | 21.0  | 4725  | 1.0036          | 0.8617   |
| 0.0016        | 22.0  | 4950  | 0.9822          | 0.8467   |
| 0.0233        | 23.0  | 5175  | 0.9225          | 0.845    |
| 0.0233        | 24.0  | 5400  | 1.0565          | 0.8483   |
| 0.0482        | 25.0  | 5625  | 1.2063          | 0.835    |
| 0.005         | 26.0  | 5850  | 1.0210          | 0.8467   |
| 0.0014        | 27.0  | 6075  | 1.0482          | 0.8517   |
| 0.0002        | 28.0  | 6300  | 1.2044          | 0.8383   |
| 0.0037        | 29.0  | 6525  | 1.0861          | 0.8533   |
| 0.0174        | 30.0  | 6750  | 1.1444          | 0.85     |
| 0.0           | 31.0  | 6975  | 1.2721          | 0.8567   |
| 0.0109        | 32.0  | 7200  | 1.2042          | 0.8583   |
| 0.0095        | 33.0  | 7425  | 1.1796          | 0.8517   |
| 0.0112        | 34.0  | 7650  | 1.2147          | 0.8583   |
| 0.0005        | 35.0  | 7875  | 1.2717          | 0.85     |
| 0.0001        | 36.0  | 8100  | 1.3234          | 0.8517   |
| 0.0015        | 37.0  | 8325  | 1.3845          | 0.8567   |
| 0.0007        | 38.0  | 8550  | 1.3111          | 0.8583   |
| 0.0           | 39.0  | 8775  | 1.3423          | 0.8567   |
| 0.0           | 40.0  | 9000  | 1.3863          | 0.855    |
| 0.0           | 41.0  | 9225  | 1.3890          | 0.8567   |
| 0.0025        | 42.0  | 9450  | 1.5279          | 0.855    |
| 0.0           | 43.0  | 9675  | 1.5233          | 0.855    |
| 0.0023        | 44.0  | 9900  | 1.5389          | 0.8567   |
| 0.0           | 45.0  | 10125 | 1.5451          | 0.8517   |
| 0.0           | 46.0  | 10350 | 1.5273          | 0.8517   |
| 0.0           | 47.0  | 10575 | 1.5407          | 0.8517   |
| 0.0           | 48.0  | 10800 | 1.5468          | 0.8517   |
| 0.0           | 49.0  | 11025 | 1.5542          | 0.8533   |
| 0.0           | 50.0  | 11250 | 1.5511          | 0.8533   |


### 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