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# smids_10x_deit_tiny_sgd_001_fold3

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2982
- Accuracy: 0.895

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5867        | 1.0   | 750   | 0.5827          | 0.7783   |
| 0.3913        | 2.0   | 1500  | 0.4291          | 0.83     |
| 0.3437        | 3.0   | 2250  | 0.3734          | 0.86     |
| 0.3224        | 4.0   | 3000  | 0.3340          | 0.8633   |
| 0.3802        | 5.0   | 3750  | 0.3192          | 0.875    |
| 0.3066        | 6.0   | 4500  | 0.3104          | 0.88     |
| 0.2589        | 7.0   | 5250  | 0.2967          | 0.8867   |
| 0.2794        | 8.0   | 6000  | 0.2987          | 0.8867   |
| 0.1833        | 9.0   | 6750  | 0.2867          | 0.8933   |
| 0.2023        | 10.0  | 7500  | 0.2817          | 0.9      |
| 0.2616        | 11.0  | 8250  | 0.2809          | 0.8883   |
| 0.2286        | 12.0  | 9000  | 0.2812          | 0.8983   |
| 0.191         | 13.0  | 9750  | 0.2821          | 0.895    |
| 0.2573        | 14.0  | 10500 | 0.2824          | 0.895    |
| 0.233         | 15.0  | 11250 | 0.2788          | 0.9033   |
| 0.227         | 16.0  | 12000 | 0.2755          | 0.9133   |
| 0.2065        | 17.0  | 12750 | 0.2819          | 0.8933   |
| 0.1957        | 18.0  | 13500 | 0.2734          | 0.9033   |
| 0.1915        | 19.0  | 14250 | 0.2738          | 0.9017   |
| 0.1774        | 20.0  | 15000 | 0.2840          | 0.8967   |
| 0.1639        | 21.0  | 15750 | 0.2800          | 0.9      |
| 0.18          | 22.0  | 16500 | 0.2722          | 0.9033   |
| 0.1754        | 23.0  | 17250 | 0.2797          | 0.8983   |
| 0.1721        | 24.0  | 18000 | 0.2818          | 0.8967   |
| 0.2322        | 25.0  | 18750 | 0.2867          | 0.8933   |
| 0.1833        | 26.0  | 19500 | 0.2854          | 0.8933   |
| 0.0838        | 27.0  | 20250 | 0.2833          | 0.9083   |
| 0.1291        | 28.0  | 21000 | 0.2872          | 0.8883   |
| 0.1475        | 29.0  | 21750 | 0.2853          | 0.8933   |
| 0.1339        | 30.0  | 22500 | 0.2879          | 0.8917   |
| 0.0869        | 31.0  | 23250 | 0.2884          | 0.895    |
| 0.1341        | 32.0  | 24000 | 0.2859          | 0.89     |
| 0.1322        | 33.0  | 24750 | 0.2895          | 0.8933   |
| 0.1482        | 34.0  | 25500 | 0.2910          | 0.8933   |
| 0.1123        | 35.0  | 26250 | 0.2921          | 0.8933   |
| 0.1145        | 36.0  | 27000 | 0.2928          | 0.8933   |
| 0.1372        | 37.0  | 27750 | 0.2965          | 0.8917   |
| 0.1907        | 38.0  | 28500 | 0.2941          | 0.8917   |
| 0.1101        | 39.0  | 29250 | 0.2932          | 0.89     |
| 0.1502        | 40.0  | 30000 | 0.2921          | 0.895    |
| 0.1006        | 41.0  | 30750 | 0.2941          | 0.8983   |
| 0.1237        | 42.0  | 31500 | 0.2961          | 0.8967   |
| 0.0943        | 43.0  | 32250 | 0.2963          | 0.895    |
| 0.1038        | 44.0  | 33000 | 0.2980          | 0.8983   |
| 0.1286        | 45.0  | 33750 | 0.2956          | 0.8917   |
| 0.0851        | 46.0  | 34500 | 0.2954          | 0.8917   |
| 0.1551        | 47.0  | 35250 | 0.2984          | 0.8917   |
| 0.0707        | 48.0  | 36000 | 0.2985          | 0.8967   |
| 0.143         | 49.0  | 36750 | 0.2982          | 0.8967   |
| 0.1125        | 50.0  | 37500 | 0.2982          | 0.895    |


### 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