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# smids_5x_beit_base_sgd_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: 0.4509
- Accuracy: 0.8217

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.119         | 1.0   | 375   | 1.2346          | 0.355    |
| 1.0823        | 2.0   | 750   | 1.1546          | 0.3883   |
| 0.9626        | 3.0   | 1125  | 1.0734          | 0.4317   |
| 0.8877        | 4.0   | 1500  | 0.9982          | 0.49     |
| 0.8704        | 5.0   | 1875  | 0.9307          | 0.5383   |
| 0.82          | 6.0   | 2250  | 0.8704          | 0.5833   |
| 0.7688        | 7.0   | 2625  | 0.8174          | 0.63     |
| 0.7442        | 8.0   | 3000  | 0.7715          | 0.6583   |
| 0.726         | 9.0   | 3375  | 0.7322          | 0.6833   |
| 0.6394        | 10.0  | 3750  | 0.6995          | 0.695    |
| 0.6379        | 11.0  | 4125  | 0.6704          | 0.7167   |
| 0.6169        | 12.0  | 4500  | 0.6463          | 0.735    |
| 0.5956        | 13.0  | 4875  | 0.6254          | 0.7483   |
| 0.617         | 14.0  | 5250  | 0.6064          | 0.7617   |
| 0.6108        | 15.0  | 5625  | 0.5908          | 0.7717   |
| 0.578         | 16.0  | 6000  | 0.5757          | 0.7767   |
| 0.591         | 17.0  | 6375  | 0.5636          | 0.7817   |
| 0.5966        | 18.0  | 6750  | 0.5528          | 0.785    |
| 0.6007        | 19.0  | 7125  | 0.5440          | 0.7883   |
| 0.5282        | 20.0  | 7500  | 0.5352          | 0.7983   |
| 0.5197        | 21.0  | 7875  | 0.5276          | 0.8033   |
| 0.5125        | 22.0  | 8250  | 0.5198          | 0.8067   |
| 0.5868        | 23.0  | 8625  | 0.5131          | 0.8083   |
| 0.5885        | 24.0  | 9000  | 0.5069          | 0.8117   |
| 0.5176        | 25.0  | 9375  | 0.5018          | 0.8133   |
| 0.5257        | 26.0  | 9750  | 0.4968          | 0.8167   |
| 0.563         | 27.0  | 10125 | 0.4923          | 0.8167   |
| 0.5177        | 28.0  | 10500 | 0.4880          | 0.815    |
| 0.5208        | 29.0  | 10875 | 0.4843          | 0.8183   |
| 0.4749        | 30.0  | 11250 | 0.4801          | 0.8183   |
| 0.5211        | 31.0  | 11625 | 0.4762          | 0.8167   |
| 0.5578        | 32.0  | 12000 | 0.4734          | 0.8167   |
| 0.5196        | 33.0  | 12375 | 0.4707          | 0.8183   |
| 0.5191        | 34.0  | 12750 | 0.4684          | 0.82     |
| 0.4852        | 35.0  | 13125 | 0.4662          | 0.82     |
| 0.4553        | 36.0  | 13500 | 0.4634          | 0.8183   |
| 0.4575        | 37.0  | 13875 | 0.4613          | 0.82     |
| 0.5121        | 38.0  | 14250 | 0.4600          | 0.82     |
| 0.4948        | 39.0  | 14625 | 0.4580          | 0.82     |
| 0.5112        | 40.0  | 15000 | 0.4566          | 0.82     |
| 0.5002        | 41.0  | 15375 | 0.4556          | 0.82     |
| 0.4865        | 42.0  | 15750 | 0.4545          | 0.82     |
| 0.5291        | 43.0  | 16125 | 0.4534          | 0.82     |
| 0.4479        | 44.0  | 16500 | 0.4529          | 0.82     |
| 0.4858        | 45.0  | 16875 | 0.4523          | 0.82     |
| 0.5195        | 46.0  | 17250 | 0.4518          | 0.82     |
| 0.5088        | 47.0  | 17625 | 0.4513          | 0.8217   |
| 0.4798        | 48.0  | 18000 | 0.4511          | 0.8217   |
| 0.4938        | 49.0  | 18375 | 0.4509          | 0.8217   |
| 0.4932        | 50.0  | 18750 | 0.4509          | 0.8217   |


### 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