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# smids_3x_beit_base_sgd_001_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: 0.3078
- Accuracy: 0.8686

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8275        | 1.0   | 225   | 0.8216          | 0.6506   |
| 0.6191        | 2.0   | 450   | 0.6029          | 0.7704   |
| 0.5649        | 3.0   | 675   | 0.5207          | 0.7953   |
| 0.5387        | 4.0   | 900   | 0.4780          | 0.8070   |
| 0.4277        | 5.0   | 1125  | 0.4562          | 0.8020   |
| 0.4656        | 6.0   | 1350  | 0.4304          | 0.8170   |
| 0.4051        | 7.0   | 1575  | 0.4146          | 0.8220   |
| 0.4601        | 8.0   | 1800  | 0.4030          | 0.8353   |
| 0.3696        | 9.0   | 2025  | 0.3828          | 0.8369   |
| 0.3307        | 10.0  | 2250  | 0.3745          | 0.8403   |
| 0.3955        | 11.0  | 2475  | 0.3651          | 0.8386   |
| 0.3579        | 12.0  | 2700  | 0.3632          | 0.8419   |
| 0.3293        | 13.0  | 2925  | 0.3578          | 0.8536   |
| 0.3333        | 14.0  | 3150  | 0.3518          | 0.8502   |
| 0.3685        | 15.0  | 3375  | 0.3389          | 0.8619   |
| 0.3757        | 16.0  | 3600  | 0.3549          | 0.8519   |
| 0.2934        | 17.0  | 3825  | 0.3349          | 0.8619   |
| 0.3154        | 18.0  | 4050  | 0.3310          | 0.8586   |
| 0.2645        | 19.0  | 4275  | 0.3332          | 0.8602   |
| 0.3308        | 20.0  | 4500  | 0.3318          | 0.8536   |
| 0.3463        | 21.0  | 4725  | 0.3245          | 0.8669   |
| 0.3576        | 22.0  | 4950  | 0.3244          | 0.8602   |
| 0.3535        | 23.0  | 5175  | 0.3229          | 0.8652   |
| 0.3392        | 24.0  | 5400  | 0.3212          | 0.8636   |
| 0.3203        | 25.0  | 5625  | 0.3258          | 0.8569   |
| 0.3082        | 26.0  | 5850  | 0.3176          | 0.8652   |
| 0.273         | 27.0  | 6075  | 0.3142          | 0.8652   |
| 0.2914        | 28.0  | 6300  | 0.3128          | 0.8652   |
| 0.2752        | 29.0  | 6525  | 0.3158          | 0.8602   |
| 0.2968        | 30.0  | 6750  | 0.3177          | 0.8602   |
| 0.286         | 31.0  | 6975  | 0.3206          | 0.8586   |
| 0.2639        | 32.0  | 7200  | 0.3171          | 0.8619   |
| 0.3029        | 33.0  | 7425  | 0.3130          | 0.8652   |
| 0.2671        | 34.0  | 7650  | 0.3120          | 0.8636   |
| 0.2568        | 35.0  | 7875  | 0.3117          | 0.8652   |
| 0.291         | 36.0  | 8100  | 0.3108          | 0.8669   |
| 0.2853        | 37.0  | 8325  | 0.3128          | 0.8669   |
| 0.2438        | 38.0  | 8550  | 0.3121          | 0.8652   |
| 0.2575        | 39.0  | 8775  | 0.3073          | 0.8702   |
| 0.2608        | 40.0  | 9000  | 0.3102          | 0.8652   |
| 0.2156        | 41.0  | 9225  | 0.3132          | 0.8686   |
| 0.2705        | 42.0  | 9450  | 0.3087          | 0.8669   |
| 0.2268        | 43.0  | 9675  | 0.3074          | 0.8702   |
| 0.2401        | 44.0  | 9900  | 0.3088          | 0.8686   |
| 0.2744        | 45.0  | 10125 | 0.3055          | 0.8702   |
| 0.2462        | 46.0  | 10350 | 0.3088          | 0.8719   |
| 0.2575        | 47.0  | 10575 | 0.3071          | 0.8702   |
| 0.254         | 48.0  | 10800 | 0.3073          | 0.8686   |
| 0.2358        | 49.0  | 11025 | 0.3080          | 0.8686   |
| 0.216         | 50.0  | 11250 | 0.3078          | 0.8686   |


### 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