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# smids_3x_beit_base_sgd_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: 0.5470
- Accuracy: 0.7867

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2271        | 1.0   | 225   | 1.2660          | 0.34     |
| 1.1764        | 2.0   | 450   | 1.2039          | 0.36     |
| 1.0866        | 3.0   | 675   | 1.1482          | 0.3833   |
| 1.02          | 4.0   | 900   | 1.0954          | 0.41     |
| 0.9521        | 5.0   | 1125  | 1.0436          | 0.4433   |
| 0.9373        | 6.0   | 1350  | 0.9954          | 0.485    |
| 0.8962        | 7.0   | 1575  | 0.9512          | 0.5317   |
| 0.8694        | 8.0   | 1800  | 0.9106          | 0.5767   |
| 0.8253        | 9.0   | 2025  | 0.8739          | 0.5967   |
| 0.8297        | 10.0  | 2250  | 0.8416          | 0.635    |
| 0.8158        | 11.0  | 2475  | 0.8130          | 0.6633   |
| 0.75          | 12.0  | 2700  | 0.7869          | 0.685    |
| 0.7851        | 13.0  | 2925  | 0.7633          | 0.69     |
| 0.761         | 14.0  | 3150  | 0.7425          | 0.7017   |
| 0.6927        | 15.0  | 3375  | 0.7233          | 0.7117   |
| 0.7078        | 16.0  | 3600  | 0.7069          | 0.7217   |
| 0.698         | 17.0  | 3825  | 0.6913          | 0.7283   |
| 0.6847        | 18.0  | 4050  | 0.6778          | 0.7367   |
| 0.6863        | 19.0  | 4275  | 0.6656          | 0.7383   |
| 0.6396        | 20.0  | 4500  | 0.6548          | 0.7417   |
| 0.6511        | 21.0  | 4725  | 0.6448          | 0.745    |
| 0.6297        | 22.0  | 4950  | 0.6350          | 0.7517   |
| 0.6013        | 23.0  | 5175  | 0.6267          | 0.755    |
| 0.635         | 24.0  | 5400  | 0.6187          | 0.76     |
| 0.6174        | 25.0  | 5625  | 0.6116          | 0.7583   |
| 0.6201        | 26.0  | 5850  | 0.6053          | 0.7617   |
| 0.5888        | 27.0  | 6075  | 0.5991          | 0.7617   |
| 0.5833        | 28.0  | 6300  | 0.5934          | 0.7633   |
| 0.6387        | 29.0  | 6525  | 0.5887          | 0.7683   |
| 0.5339        | 30.0  | 6750  | 0.5839          | 0.7717   |
| 0.5756        | 31.0  | 6975  | 0.5797          | 0.7767   |
| 0.6386        | 32.0  | 7200  | 0.5758          | 0.775    |
| 0.6245        | 33.0  | 7425  | 0.5722          | 0.775    |
| 0.5779        | 34.0  | 7650  | 0.5690          | 0.7767   |
| 0.57          | 35.0  | 7875  | 0.5661          | 0.7767   |
| 0.5776        | 36.0  | 8100  | 0.5632          | 0.7767   |
| 0.5861        | 37.0  | 8325  | 0.5611          | 0.7767   |
| 0.5518        | 38.0  | 8550  | 0.5586          | 0.7767   |
| 0.604         | 39.0  | 8775  | 0.5567          | 0.7817   |
| 0.539         | 40.0  | 9000  | 0.5549          | 0.7833   |
| 0.5457        | 41.0  | 9225  | 0.5534          | 0.7833   |
| 0.6155        | 42.0  | 9450  | 0.5518          | 0.785    |
| 0.5379        | 43.0  | 9675  | 0.5506          | 0.785    |
| 0.5848        | 44.0  | 9900  | 0.5496          | 0.7867   |
| 0.5814        | 45.0  | 10125 | 0.5488          | 0.7867   |
| 0.5255        | 46.0  | 10350 | 0.5481          | 0.7867   |
| 0.5726        | 47.0  | 10575 | 0.5476          | 0.7867   |
| 0.5762        | 48.0  | 10800 | 0.5473          | 0.7867   |
| 0.6192        | 49.0  | 11025 | 0.5471          | 0.7867   |
| 0.5747        | 50.0  | 11250 | 0.5470          | 0.7867   |


### 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