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# smids_5x_deit_small_sgd_0001_fold1

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5286
- Accuracy: 0.7913

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0382        | 1.0   | 376   | 1.0714          | 0.4090   |
| 1.0394        | 2.0   | 752   | 1.0356          | 0.4491   |
| 0.9541        | 3.0   | 1128  | 1.0000          | 0.5042   |
| 0.928         | 4.0   | 1504  | 0.9637          | 0.5559   |
| 0.8813        | 5.0   | 1880  | 0.9285          | 0.5943   |
| 0.8344        | 6.0   | 2256  | 0.8967          | 0.6227   |
| 0.7873        | 7.0   | 2632  | 0.8681          | 0.6361   |
| 0.8127        | 8.0   | 3008  | 0.8414          | 0.6544   |
| 0.8077        | 9.0   | 3384  | 0.8166          | 0.6628   |
| 0.7559        | 10.0  | 3760  | 0.7932          | 0.6811   |
| 0.7293        | 11.0  | 4136  | 0.7713          | 0.6962   |
| 0.7184        | 12.0  | 4512  | 0.7510          | 0.7028   |
| 0.6866        | 13.0  | 4888  | 0.7320          | 0.7078   |
| 0.6588        | 14.0  | 5264  | 0.7145          | 0.7195   |
| 0.6425        | 15.0  | 5640  | 0.6984          | 0.7245   |
| 0.6378        | 16.0  | 6016  | 0.6836          | 0.7312   |
| 0.5876        | 17.0  | 6392  | 0.6699          | 0.7396   |
| 0.6379        | 18.0  | 6768  | 0.6573          | 0.7429   |
| 0.6063        | 19.0  | 7144  | 0.6456          | 0.7479   |
| 0.5557        | 20.0  | 7520  | 0.6350          | 0.7496   |
| 0.5709        | 21.0  | 7896  | 0.6253          | 0.7513   |
| 0.5404        | 22.0  | 8272  | 0.6166          | 0.7563   |
| 0.5599        | 23.0  | 8648  | 0.6082          | 0.7529   |
| 0.5567        | 24.0  | 9024  | 0.6008          | 0.7613   |
| 0.5445        | 25.0  | 9400  | 0.5938          | 0.7646   |
| 0.5273        | 26.0  | 9776  | 0.5874          | 0.7629   |
| 0.5187        | 27.0  | 10152 | 0.5814          | 0.7613   |
| 0.4686        | 28.0  | 10528 | 0.5760          | 0.7629   |
| 0.502         | 29.0  | 10904 | 0.5710          | 0.7629   |
| 0.5086        | 30.0  | 11280 | 0.5663          | 0.7663   |
| 0.5383        | 31.0  | 11656 | 0.5621          | 0.7679   |
| 0.5306        | 32.0  | 12032 | 0.5581          | 0.7696   |
| 0.4719        | 33.0  | 12408 | 0.5545          | 0.7713   |
| 0.4733        | 34.0  | 12784 | 0.5512          | 0.7763   |
| 0.4916        | 35.0  | 13160 | 0.5482          | 0.7796   |
| 0.4659        | 36.0  | 13536 | 0.5454          | 0.7796   |
| 0.4447        | 37.0  | 13912 | 0.5429          | 0.7830   |
| 0.5196        | 38.0  | 14288 | 0.5406          | 0.7830   |
| 0.4685        | 39.0  | 14664 | 0.5386          | 0.7830   |
| 0.4526        | 40.0  | 15040 | 0.5367          | 0.7830   |
| 0.4896        | 41.0  | 15416 | 0.5350          | 0.7863   |
| 0.4446        | 42.0  | 15792 | 0.5336          | 0.7863   |
| 0.4328        | 43.0  | 16168 | 0.5323          | 0.7863   |
| 0.5156        | 44.0  | 16544 | 0.5312          | 0.7880   |
| 0.4252        | 45.0  | 16920 | 0.5303          | 0.7896   |
| 0.4576        | 46.0  | 17296 | 0.5296          | 0.7896   |
| 0.4261        | 47.0  | 17672 | 0.5291          | 0.7913   |
| 0.4841        | 48.0  | 18048 | 0.5288          | 0.7913   |
| 0.4563        | 49.0  | 18424 | 0.5286          | 0.7913   |
| 0.4361        | 50.0  | 18800 | 0.5286          | 0.7913   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm