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# smids_3x_beit_base_adamax_001_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: 0.9113
- Accuracy: 0.8833

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7164        | 1.0   | 225   | 0.4684          | 0.81     |
| 0.3974        | 2.0   | 450   | 0.3760          | 0.845    |
| 0.3042        | 3.0   | 675   | 0.4562          | 0.82     |
| 0.264         | 4.0   | 900   | 0.3521          | 0.86     |
| 0.2635        | 5.0   | 1125  | 0.3585          | 0.8567   |
| 0.3122        | 6.0   | 1350  | 0.3482          | 0.87     |
| 0.1881        | 7.0   | 1575  | 0.4250          | 0.8583   |
| 0.2288        | 8.0   | 1800  | 0.4228          | 0.8583   |
| 0.1644        | 9.0   | 2025  | 0.5487          | 0.8367   |
| 0.1666        | 10.0  | 2250  | 0.4820          | 0.8467   |
| 0.1186        | 11.0  | 2475  | 0.6337          | 0.835    |
| 0.1307        | 12.0  | 2700  | 0.4076          | 0.87     |
| 0.0842        | 13.0  | 2925  | 0.5631          | 0.8733   |
| 0.0933        | 14.0  | 3150  | 0.5566          | 0.8767   |
| 0.0383        | 15.0  | 3375  | 0.6882          | 0.8433   |
| 0.0107        | 16.0  | 3600  | 0.5512          | 0.87     |
| 0.0331        | 17.0  | 3825  | 0.5868          | 0.8617   |
| 0.0654        | 18.0  | 4050  | 0.7675          | 0.8517   |
| 0.0588        | 19.0  | 4275  | 0.5953          | 0.8833   |
| 0.0197        | 20.0  | 4500  | 0.6863          | 0.875    |
| 0.0147        | 21.0  | 4725  | 0.7719          | 0.8717   |
| 0.0638        | 22.0  | 4950  | 0.7585          | 0.87     |
| 0.0213        | 23.0  | 5175  | 0.7631          | 0.8667   |
| 0.0027        | 24.0  | 5400  | 0.8123          | 0.8717   |
| 0.0619        | 25.0  | 5625  | 0.6777          | 0.87     |
| 0.0044        | 26.0  | 5850  | 0.7468          | 0.8833   |
| 0.0118        | 27.0  | 6075  | 0.7959          | 0.8683   |
| 0.0014        | 28.0  | 6300  | 0.6725          | 0.8733   |
| 0.0196        | 29.0  | 6525  | 0.8072          | 0.8733   |
| 0.0092        | 30.0  | 6750  | 0.7937          | 0.8833   |
| 0.0065        | 31.0  | 6975  | 0.9261          | 0.875    |
| 0.0008        | 32.0  | 7200  | 0.8949          | 0.875    |
| 0.0001        | 33.0  | 7425  | 0.8856          | 0.89     |
| 0.0027        | 34.0  | 7650  | 0.8960          | 0.8633   |
| 0.0           | 35.0  | 7875  | 0.9060          | 0.87     |
| 0.0           | 36.0  | 8100  | 0.8882          | 0.875    |
| 0.0044        | 37.0  | 8325  | 0.9127          | 0.8783   |
| 0.0           | 38.0  | 8550  | 0.9987          | 0.8767   |
| 0.0           | 39.0  | 8775  | 0.9306          | 0.8817   |
| 0.0           | 40.0  | 9000  | 0.8606          | 0.885    |
| 0.0           | 41.0  | 9225  | 0.8647          | 0.8817   |
| 0.0           | 42.0  | 9450  | 0.8530          | 0.88     |
| 0.0           | 43.0  | 9675  | 0.8745          | 0.885    |
| 0.0           | 44.0  | 9900  | 0.8799          | 0.8817   |
| 0.0           | 45.0  | 10125 | 0.9191          | 0.87     |
| 0.0           | 46.0  | 10350 | 0.9238          | 0.88     |
| 0.0033        | 47.0  | 10575 | 0.9260          | 0.8783   |
| 0.0           | 48.0  | 10800 | 0.9161          | 0.8767   |
| 0.0           | 49.0  | 11025 | 0.9134          | 0.88     |
| 0.0           | 50.0  | 11250 | 0.9113          | 0.8833   |


### 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