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# smids_10x_deit_small_sgd_001_fold3

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: 0.2811
- Accuracy: 0.9083

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.545         | 1.0   | 750   | 0.5587          | 0.785    |
| 0.4133        | 2.0   | 1500  | 0.4211          | 0.8467   |
| 0.358         | 3.0   | 2250  | 0.3782          | 0.8633   |
| 0.3237        | 4.0   | 3000  | 0.3490          | 0.87     |
| 0.3443        | 5.0   | 3750  | 0.3305          | 0.8767   |
| 0.2928        | 6.0   | 4500  | 0.3200          | 0.8817   |
| 0.2686        | 7.0   | 5250  | 0.3122          | 0.8867   |
| 0.2534        | 8.0   | 6000  | 0.3123          | 0.885    |
| 0.2251        | 9.0   | 6750  | 0.2946          | 0.8933   |
| 0.1954        | 10.0  | 7500  | 0.2908          | 0.9      |
| 0.2504        | 11.0  | 8250  | 0.2911          | 0.8967   |
| 0.2172        | 12.0  | 9000  | 0.2849          | 0.905    |
| 0.2089        | 13.0  | 9750  | 0.2810          | 0.905    |
| 0.2631        | 14.0  | 10500 | 0.2804          | 0.905    |
| 0.2076        | 15.0  | 11250 | 0.2751          | 0.915    |
| 0.1833        | 16.0  | 12000 | 0.2763          | 0.9067   |
| 0.2051        | 17.0  | 12750 | 0.2775          | 0.905    |
| 0.1927        | 18.0  | 13500 | 0.2752          | 0.9083   |
| 0.1896        | 19.0  | 14250 | 0.2722          | 0.9117   |
| 0.193         | 20.0  | 15000 | 0.2720          | 0.905    |
| 0.1978        | 21.0  | 15750 | 0.2723          | 0.905    |
| 0.193         | 22.0  | 16500 | 0.2691          | 0.91     |
| 0.1867        | 23.0  | 17250 | 0.2706          | 0.9133   |
| 0.1588        | 24.0  | 18000 | 0.2753          | 0.9083   |
| 0.1896        | 25.0  | 18750 | 0.2771          | 0.8983   |
| 0.1697        | 26.0  | 19500 | 0.2708          | 0.9133   |
| 0.1259        | 27.0  | 20250 | 0.2702          | 0.9117   |
| 0.152         | 28.0  | 21000 | 0.2731          | 0.9083   |
| 0.1891        | 29.0  | 21750 | 0.2747          | 0.9117   |
| 0.1716        | 30.0  | 22500 | 0.2723          | 0.9083   |
| 0.1252        | 31.0  | 23250 | 0.2778          | 0.905    |
| 0.1227        | 32.0  | 24000 | 0.2742          | 0.9083   |
| 0.166         | 33.0  | 24750 | 0.2738          | 0.9017   |
| 0.1299        | 34.0  | 25500 | 0.2772          | 0.9083   |
| 0.1287        | 35.0  | 26250 | 0.2752          | 0.91     |
| 0.1172        | 36.0  | 27000 | 0.2784          | 0.9033   |
| 0.1292        | 37.0  | 27750 | 0.2763          | 0.9033   |
| 0.1686        | 38.0  | 28500 | 0.2772          | 0.9067   |
| 0.1469        | 39.0  | 29250 | 0.2777          | 0.9067   |
| 0.1673        | 40.0  | 30000 | 0.2785          | 0.9083   |
| 0.1244        | 41.0  | 30750 | 0.2779          | 0.9067   |
| 0.149         | 42.0  | 31500 | 0.2782          | 0.9067   |
| 0.1031        | 43.0  | 32250 | 0.2799          | 0.905    |
| 0.1374        | 44.0  | 33000 | 0.2832          | 0.9067   |
| 0.1179        | 45.0  | 33750 | 0.2818          | 0.905    |
| 0.1282        | 46.0  | 34500 | 0.2810          | 0.905    |
| 0.1603        | 47.0  | 35250 | 0.2819          | 0.9067   |
| 0.1237        | 48.0  | 36000 | 0.2811          | 0.9083   |
| 0.1333        | 49.0  | 36750 | 0.2808          | 0.9067   |
| 0.1344        | 50.0  | 37500 | 0.2811          | 0.9083   |


### 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