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# smids_5x_beit_base_sgd_0001_fold1

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: 0.5159
- Accuracy: 0.7947

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1623        | 1.0   | 376   | 1.2650          | 0.3406   |
| 1.0872        | 2.0   | 752   | 1.1797          | 0.3656   |
| 0.9959        | 3.0   | 1128  | 1.0972          | 0.4040   |
| 0.9117        | 4.0   | 1504  | 1.0182          | 0.4641   |
| 0.8307        | 5.0   | 1880  | 0.9466          | 0.5359   |
| 0.8065        | 6.0   | 2256  | 0.8845          | 0.5943   |
| 0.7607        | 7.0   | 2632  | 0.8316          | 0.6277   |
| 0.7796        | 8.0   | 3008  | 0.7902          | 0.6477   |
| 0.7103        | 9.0   | 3384  | 0.7560          | 0.6611   |
| 0.6802        | 10.0  | 3760  | 0.7267          | 0.6745   |
| 0.6695        | 11.0  | 4136  | 0.7035          | 0.6945   |
| 0.6571        | 12.0  | 4512  | 0.6839          | 0.6995   |
| 0.6174        | 13.0  | 4888  | 0.6660          | 0.7045   |
| 0.6036        | 14.0  | 5264  | 0.6517          | 0.7145   |
| 0.6026        | 15.0  | 5640  | 0.6384          | 0.7212   |
| 0.5646        | 16.0  | 6016  | 0.6262          | 0.7312   |
| 0.5262        | 17.0  | 6392  | 0.6162          | 0.7396   |
| 0.5941        | 18.0  | 6768  | 0.6069          | 0.7412   |
| 0.5547        | 19.0  | 7144  | 0.5985          | 0.7513   |
| 0.5277        | 20.0  | 7520  | 0.5904          | 0.7529   |
| 0.5447        | 21.0  | 7896  | 0.5840          | 0.7563   |
| 0.4895        | 22.0  | 8272  | 0.5780          | 0.7613   |
| 0.5534        | 23.0  | 8648  | 0.5722          | 0.7613   |
| 0.5434        | 24.0  | 9024  | 0.5667          | 0.7629   |
| 0.5157        | 25.0  | 9400  | 0.5616          | 0.7696   |
| 0.521         | 26.0  | 9776  | 0.5574          | 0.7763   |
| 0.5165        | 27.0  | 10152 | 0.5535          | 0.7780   |
| 0.4725        | 28.0  | 10528 | 0.5496          | 0.7796   |
| 0.5119        | 29.0  | 10904 | 0.5463          | 0.7780   |
| 0.5112        | 30.0  | 11280 | 0.5429          | 0.7746   |
| 0.5018        | 31.0  | 11656 | 0.5394          | 0.7796   |
| 0.5237        | 32.0  | 12032 | 0.5367          | 0.7846   |
| 0.4645        | 33.0  | 12408 | 0.5340          | 0.7863   |
| 0.4409        | 34.0  | 12784 | 0.5315          | 0.7896   |
| 0.4837        | 35.0  | 13160 | 0.5294          | 0.7880   |
| 0.4766        | 36.0  | 13536 | 0.5273          | 0.7863   |
| 0.4405        | 37.0  | 13912 | 0.5255          | 0.7880   |
| 0.4999        | 38.0  | 14288 | 0.5239          | 0.7930   |
| 0.4612        | 39.0  | 14664 | 0.5226          | 0.7913   |
| 0.4901        | 40.0  | 15040 | 0.5213          | 0.7913   |
| 0.5002        | 41.0  | 15416 | 0.5202          | 0.7930   |
| 0.4394        | 42.0  | 15792 | 0.5193          | 0.7930   |
| 0.4541        | 43.0  | 16168 | 0.5184          | 0.7913   |
| 0.463         | 44.0  | 16544 | 0.5178          | 0.7947   |
| 0.4704        | 45.0  | 16920 | 0.5171          | 0.7930   |
| 0.5003        | 46.0  | 17296 | 0.5167          | 0.7913   |
| 0.4732        | 47.0  | 17672 | 0.5163          | 0.7947   |
| 0.4967        | 48.0  | 18048 | 0.5161          | 0.7947   |
| 0.455         | 49.0  | 18424 | 0.5159          | 0.7947   |
| 0.4609        | 50.0  | 18800 | 0.5159          | 0.7947   |


### 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