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# smids_3x_deit_small_adamax_00001_fold5

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: 0.8664
- Accuracy: 0.8867

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3734        | 1.0   | 225   | 0.3687          | 0.8433   |
| 0.2659        | 2.0   | 450   | 0.3135          | 0.8733   |
| 0.1931        | 3.0   | 675   | 0.3034          | 0.8767   |
| 0.143         | 4.0   | 900   | 0.2889          | 0.885    |
| 0.131         | 5.0   | 1125  | 0.3167          | 0.8833   |
| 0.0729        | 6.0   | 1350  | 0.3187          | 0.8867   |
| 0.0909        | 7.0   | 1575  | 0.3427          | 0.8833   |
| 0.081         | 8.0   | 1800  | 0.3465          | 0.885    |
| 0.0722        | 9.0   | 2025  | 0.4276          | 0.8833   |
| 0.0153        | 10.0  | 2250  | 0.5104          | 0.8767   |
| 0.0073        | 11.0  | 2475  | 0.5571          | 0.8817   |
| 0.0388        | 12.0  | 2700  | 0.5705          | 0.895    |
| 0.0128        | 13.0  | 2925  | 0.6043          | 0.8767   |
| 0.0017        | 14.0  | 3150  | 0.6203          | 0.8867   |
| 0.0016        | 15.0  | 3375  | 0.6463          | 0.8883   |
| 0.0003        | 16.0  | 3600  | 0.6591          | 0.8867   |
| 0.0001        | 17.0  | 3825  | 0.6914          | 0.8867   |
| 0.0002        | 18.0  | 4050  | 0.7053          | 0.885    |
| 0.0391        | 19.0  | 4275  | 0.7283          | 0.8867   |
| 0.0001        | 20.0  | 4500  | 0.7538          | 0.885    |
| 0.0001        | 21.0  | 4725  | 0.7610          | 0.8833   |
| 0.0001        | 22.0  | 4950  | 0.7539          | 0.885    |
| 0.0001        | 23.0  | 5175  | 0.7650          | 0.8883   |
| 0.0242        | 24.0  | 5400  | 0.7853          | 0.8817   |
| 0.0001        | 25.0  | 5625  | 0.7908          | 0.8833   |
| 0.0001        | 26.0  | 5850  | 0.7914          | 0.885    |
| 0.0001        | 27.0  | 6075  | 0.8136          | 0.8817   |
| 0.0           | 28.0  | 6300  | 0.8276          | 0.8817   |
| 0.0067        | 29.0  | 6525  | 0.8343          | 0.8817   |
| 0.0           | 30.0  | 6750  | 0.8302          | 0.885    |
| 0.0           | 31.0  | 6975  | 0.8207          | 0.885    |
| 0.0           | 32.0  | 7200  | 0.8405          | 0.8833   |
| 0.0056        | 33.0  | 7425  | 0.8445          | 0.8833   |
| 0.0           | 34.0  | 7650  | 0.8188          | 0.885    |
| 0.0026        | 35.0  | 7875  | 0.8428          | 0.885    |
| 0.0           | 36.0  | 8100  | 0.8431          | 0.8867   |
| 0.0072        | 37.0  | 8325  | 0.8465          | 0.8867   |
| 0.0           | 38.0  | 8550  | 0.8730          | 0.885    |
| 0.0           | 39.0  | 8775  | 0.8549          | 0.8867   |
| 0.0           | 40.0  | 9000  | 0.8617          | 0.8833   |
| 0.0           | 41.0  | 9225  | 0.8684          | 0.885    |
| 0.0           | 42.0  | 9450  | 0.8688          | 0.885    |
| 0.0038        | 43.0  | 9675  | 0.8699          | 0.885    |
| 0.0           | 44.0  | 9900  | 0.8640          | 0.8867   |
| 0.0           | 45.0  | 10125 | 0.8568          | 0.8867   |
| 0.0           | 46.0  | 10350 | 0.8661          | 0.885    |
| 0.0           | 47.0  | 10575 | 0.8676          | 0.8867   |
| 0.0           | 48.0  | 10800 | 0.8675          | 0.8867   |
| 0.0           | 49.0  | 11025 | 0.8664          | 0.8867   |
| 0.0           | 50.0  | 11250 | 0.8664          | 0.8867   |


### 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