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# smids_3x_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: 0.3132
- Accuracy: 0.885

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9334        | 1.0   | 225   | 0.9342          | 0.5617   |
| 0.6594        | 2.0   | 450   | 0.7339          | 0.7033   |
| 0.5574        | 3.0   | 675   | 0.6026          | 0.7783   |
| 0.5172        | 4.0   | 900   | 0.5194          | 0.82     |
| 0.484         | 5.0   | 1125  | 0.4765          | 0.83     |
| 0.4213        | 6.0   | 1350  | 0.4411          | 0.8317   |
| 0.3641        | 7.0   | 1575  | 0.4119          | 0.84     |
| 0.3918        | 8.0   | 1800  | 0.3965          | 0.8467   |
| 0.3897        | 9.0   | 2025  | 0.3767          | 0.855    |
| 0.3608        | 10.0  | 2250  | 0.3717          | 0.855    |
| 0.3065        | 11.0  | 2475  | 0.3577          | 0.8583   |
| 0.3443        | 12.0  | 2700  | 0.3519          | 0.8667   |
| 0.3224        | 13.0  | 2925  | 0.3447          | 0.8667   |
| 0.3208        | 14.0  | 3150  | 0.3383          | 0.8767   |
| 0.2793        | 15.0  | 3375  | 0.3349          | 0.8783   |
| 0.2956        | 16.0  | 3600  | 0.3330          | 0.875    |
| 0.3399        | 17.0  | 3825  | 0.3298          | 0.8783   |
| 0.2685        | 18.0  | 4050  | 0.3281          | 0.8733   |
| 0.2915        | 19.0  | 4275  | 0.3329          | 0.8767   |
| 0.2697        | 20.0  | 4500  | 0.3263          | 0.8767   |
| 0.2328        | 21.0  | 4725  | 0.3232          | 0.8767   |
| 0.2125        | 22.0  | 4950  | 0.3202          | 0.8783   |
| 0.2061        | 23.0  | 5175  | 0.3195          | 0.8817   |
| 0.2429        | 24.0  | 5400  | 0.3174          | 0.8833   |
| 0.2246        | 25.0  | 5625  | 0.3192          | 0.8867   |
| 0.2934        | 26.0  | 5850  | 0.3181          | 0.885    |
| 0.2259        | 27.0  | 6075  | 0.3160          | 0.885    |
| 0.2084        | 28.0  | 6300  | 0.3155          | 0.8833   |
| 0.2294        | 29.0  | 6525  | 0.3147          | 0.88     |
| 0.2001        | 30.0  | 6750  | 0.3145          | 0.8817   |
| 0.2211        | 31.0  | 6975  | 0.3141          | 0.8867   |
| 0.2219        | 32.0  | 7200  | 0.3133          | 0.8883   |
| 0.2508        | 33.0  | 7425  | 0.3138          | 0.8817   |
| 0.2086        | 34.0  | 7650  | 0.3129          | 0.8883   |
| 0.1887        | 35.0  | 7875  | 0.3125          | 0.8817   |
| 0.215         | 36.0  | 8100  | 0.3139          | 0.8867   |
| 0.1909        | 37.0  | 8325  | 0.3137          | 0.89     |
| 0.2136        | 38.0  | 8550  | 0.3150          | 0.8767   |
| 0.1884        | 39.0  | 8775  | 0.3120          | 0.8867   |
| 0.195         | 40.0  | 9000  | 0.3127          | 0.8833   |
| 0.178         | 41.0  | 9225  | 0.3134          | 0.885    |
| 0.1962        | 42.0  | 9450  | 0.3131          | 0.885    |
| 0.1627        | 43.0  | 9675  | 0.3128          | 0.885    |
| 0.1909        | 44.0  | 9900  | 0.3136          | 0.885    |
| 0.1981        | 45.0  | 10125 | 0.3133          | 0.885    |
| 0.1676        | 46.0  | 10350 | 0.3129          | 0.885    |
| 0.2286        | 47.0  | 10575 | 0.3130          | 0.885    |
| 0.1565        | 48.0  | 10800 | 0.3131          | 0.885    |
| 0.2098        | 49.0  | 11025 | 0.3132          | 0.885    |
| 0.1664        | 50.0  | 11250 | 0.3132          | 0.885    |


### 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