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# smids_3x_beit_base_adamax_0001_fold5

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: 0.9117
- Accuracy: 0.9083

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2915        | 1.0   | 225   | 0.2769          | 0.8883   |
| 0.1074        | 2.0   | 450   | 0.2359          | 0.91     |
| 0.1094        | 3.0   | 675   | 0.3670          | 0.8817   |
| 0.1305        | 4.0   | 900   | 0.3641          | 0.905    |
| 0.0531        | 5.0   | 1125  | 0.4414          | 0.8967   |
| 0.0329        | 6.0   | 1350  | 0.4726          | 0.91     |
| 0.0583        | 7.0   | 1575  | 0.5580          | 0.8967   |
| 0.0317        | 8.0   | 1800  | 0.6015          | 0.905    |
| 0.0116        | 9.0   | 2025  | 0.6249          | 0.9      |
| 0.0004        | 10.0  | 2250  | 0.8362          | 0.8783   |
| 0.0225        | 11.0  | 2475  | 0.6370          | 0.905    |
| 0.0342        | 12.0  | 2700  | 0.8273          | 0.8967   |
| 0.0002        | 13.0  | 2925  | 0.8223          | 0.8983   |
| 0.0011        | 14.0  | 3150  | 0.7285          | 0.9017   |
| 0.0015        | 15.0  | 3375  | 0.8002          | 0.9017   |
| 0.0005        | 16.0  | 3600  | 0.7883          | 0.8983   |
| 0.0005        | 17.0  | 3825  | 1.0010          | 0.8867   |
| 0.0374        | 18.0  | 4050  | 0.8850          | 0.89     |
| 0.0379        | 19.0  | 4275  | 0.9596          | 0.885    |
| 0.0024        | 20.0  | 4500  | 0.8051          | 0.9      |
| 0.0007        | 21.0  | 4725  | 0.8980          | 0.8917   |
| 0.0           | 22.0  | 4950  | 0.8632          | 0.8967   |
| 0.0           | 23.0  | 5175  | 0.7801          | 0.905    |
| 0.0066        | 24.0  | 5400  | 0.9114          | 0.895    |
| 0.0001        | 25.0  | 5625  | 0.8232          | 0.905    |
| 0.0001        | 26.0  | 5850  | 0.9069          | 0.8983   |
| 0.0001        | 27.0  | 6075  | 0.9702          | 0.8933   |
| 0.0           | 28.0  | 6300  | 0.9355          | 0.9017   |
| 0.007         | 29.0  | 6525  | 0.9409          | 0.8933   |
| 0.0           | 30.0  | 6750  | 0.9196          | 0.905    |
| 0.0           | 31.0  | 6975  | 0.9430          | 0.89     |
| 0.0           | 32.0  | 7200  | 0.9769          | 0.8867   |
| 0.003         | 33.0  | 7425  | 0.9480          | 0.8917   |
| 0.0           | 34.0  | 7650  | 0.8822          | 0.9033   |
| 0.001         | 35.0  | 7875  | 0.9164          | 0.8983   |
| 0.0           | 36.0  | 8100  | 0.8790          | 0.9083   |
| 0.0069        | 37.0  | 8325  | 0.9068          | 0.9017   |
| 0.0           | 38.0  | 8550  | 0.8640          | 0.9017   |
| 0.0002        | 39.0  | 8775  | 0.9430          | 0.905    |
| 0.0017        | 40.0  | 9000  | 0.9609          | 0.8983   |
| 0.0           | 41.0  | 9225  | 0.9713          | 0.8933   |
| 0.0           | 42.0  | 9450  | 0.9484          | 0.905    |
| 0.003         | 43.0  | 9675  | 0.9360          | 0.9067   |
| 0.0           | 44.0  | 9900  | 0.8845          | 0.905    |
| 0.0           | 45.0  | 10125 | 0.8663          | 0.905    |
| 0.0           | 46.0  | 10350 | 0.8862          | 0.905    |
| 0.0           | 47.0  | 10575 | 0.9026          | 0.9067   |
| 0.0           | 48.0  | 10800 | 0.9052          | 0.905    |
| 0.0           | 49.0  | 11025 | 0.9131          | 0.9083   |
| 0.0           | 50.0  | 11250 | 0.9117          | 0.9083   |


### 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