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# smids_10x_deit_base_sgd_00001_fold3

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0056
- Accuracy: 0.5583

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1044        | 1.0   | 750   | 1.1039          | 0.36     |
| 1.0936        | 2.0   | 1500  | 1.1001          | 0.3683   |
| 1.0833        | 3.0   | 2250  | 1.0965          | 0.385    |
| 1.0922        | 4.0   | 3000  | 1.0929          | 0.3867   |
| 1.0937        | 5.0   | 3750  | 1.0895          | 0.3933   |
| 1.0793        | 6.0   | 4500  | 1.0862          | 0.4083   |
| 1.0808        | 7.0   | 5250  | 1.0829          | 0.4133   |
| 1.0795        | 8.0   | 6000  | 1.0798          | 0.42     |
| 1.0735        | 9.0   | 6750  | 1.0767          | 0.4283   |
| 1.0874        | 10.0  | 7500  | 1.0736          | 0.435    |
| 1.0661        | 11.0  | 8250  | 1.0706          | 0.4483   |
| 1.038         | 12.0  | 9000  | 1.0677          | 0.445    |
| 1.0511        | 13.0  | 9750  | 1.0648          | 0.45     |
| 1.0539        | 14.0  | 10500 | 1.0620          | 0.4617   |
| 1.0408        | 15.0  | 11250 | 1.0592          | 0.46     |
| 1.0259        | 16.0  | 12000 | 1.0564          | 0.4617   |
| 1.0339        | 17.0  | 12750 | 1.0537          | 0.47     |
| 1.0416        | 18.0  | 13500 | 1.0511          | 0.4683   |
| 1.0423        | 19.0  | 14250 | 1.0484          | 0.4733   |
| 1.0333        | 20.0  | 15000 | 1.0459          | 0.4817   |
| 1.0189        | 21.0  | 15750 | 1.0434          | 0.485    |
| 1.0248        | 22.0  | 16500 | 1.0409          | 0.4917   |
| 1.0226        | 23.0  | 17250 | 1.0385          | 0.495    |
| 1.0228        | 24.0  | 18000 | 1.0362          | 0.5      |
| 1.0292        | 25.0  | 18750 | 1.0339          | 0.4983   |
| 1.02          | 26.0  | 19500 | 1.0317          | 0.5      |
| 1.0227        | 27.0  | 20250 | 1.0296          | 0.5017   |
| 1.0121        | 28.0  | 21000 | 1.0275          | 0.5083   |
| 1.0052        | 29.0  | 21750 | 1.0255          | 0.5183   |
| 0.9924        | 30.0  | 22500 | 1.0236          | 0.52     |
| 1.0074        | 31.0  | 23250 | 1.0218          | 0.5267   |
| 0.9858        | 32.0  | 24000 | 1.0200          | 0.5267   |
| 1.0028        | 33.0  | 24750 | 1.0184          | 0.5283   |
| 0.9845        | 34.0  | 25500 | 1.0169          | 0.5317   |
| 0.992         | 35.0  | 26250 | 1.0154          | 0.535    |
| 0.9814        | 36.0  | 27000 | 1.0141          | 0.535    |
| 1.0022        | 37.0  | 27750 | 1.0128          | 0.535    |
| 0.9973        | 38.0  | 28500 | 1.0117          | 0.5367   |
| 0.9952        | 39.0  | 29250 | 1.0106          | 0.54     |
| 0.9809        | 40.0  | 30000 | 1.0097          | 0.5417   |
| 0.9816        | 41.0  | 30750 | 1.0089          | 0.5467   |
| 1.0061        | 42.0  | 31500 | 1.0081          | 0.5533   |
| 0.9998        | 43.0  | 32250 | 1.0075          | 0.555    |
| 0.992         | 44.0  | 33000 | 1.0070          | 0.555    |
| 0.9773        | 45.0  | 33750 | 1.0065          | 0.5567   |
| 0.963         | 46.0  | 34500 | 1.0062          | 0.5567   |
| 0.9849        | 47.0  | 35250 | 1.0059          | 0.5567   |
| 0.9816        | 48.0  | 36000 | 1.0057          | 0.5583   |
| 0.999         | 49.0  | 36750 | 1.0056          | 0.5583   |
| 0.9748        | 50.0  | 37500 | 1.0056          | 0.5583   |


### 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