
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_3x_deit_small_adamax_0001_fold4

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: 1.1878
- Accuracy: 0.89

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.358         | 1.0   | 225   | 0.3442          | 0.86     |
| 0.0876        | 2.0   | 450   | 0.3933          | 0.8767   |
| 0.0775        | 3.0   | 675   | 0.6722          | 0.8367   |
| 0.035         | 4.0   | 900   | 0.5573          | 0.875    |
| 0.0884        | 5.0   | 1125  | 0.6478          | 0.8783   |
| 0.0046        | 6.0   | 1350  | 0.6948          | 0.8817   |
| 0.0172        | 7.0   | 1575  | 0.8233          | 0.8867   |
| 0.0024        | 8.0   | 1800  | 0.7176          | 0.8833   |
| 0.0123        | 9.0   | 2025  | 0.8547          | 0.875    |
| 0.0046        | 10.0  | 2250  | 0.9722          | 0.87     |
| 0.0001        | 11.0  | 2475  | 0.8743          | 0.89     |
| 0.0097        | 12.0  | 2700  | 1.0010          | 0.8783   |
| 0.0001        | 13.0  | 2925  | 0.9311          | 0.88     |
| 0.0002        | 14.0  | 3150  | 0.9762          | 0.8833   |
| 0.0029        | 15.0  | 3375  | 0.9797          | 0.8867   |
| 0.0           | 16.0  | 3600  | 1.0447          | 0.8867   |
| 0.0           | 17.0  | 3825  | 1.0609          | 0.8817   |
| 0.0           | 18.0  | 4050  | 1.0141          | 0.8867   |
| 0.0           | 19.0  | 4275  | 1.0239          | 0.8883   |
| 0.0           | 20.0  | 4500  | 1.0236          | 0.8867   |
| 0.0           | 21.0  | 4725  | 1.0764          | 0.8883   |
| 0.0           | 22.0  | 4950  | 1.0464          | 0.8883   |
| 0.0           | 23.0  | 5175  | 1.0429          | 0.8917   |
| 0.0           | 24.0  | 5400  | 1.0507          | 0.8883   |
| 0.0           | 25.0  | 5625  | 1.0648          | 0.8883   |
| 0.0           | 26.0  | 5850  | 1.0647          | 0.8867   |
| 0.0           | 27.0  | 6075  | 1.0952          | 0.8883   |
| 0.0           | 28.0  | 6300  | 1.1057          | 0.8867   |
| 0.0           | 29.0  | 6525  | 1.0765          | 0.8917   |
| 0.0           | 30.0  | 6750  | 1.0978          | 0.89     |
| 0.0           | 31.0  | 6975  | 1.1017          | 0.8917   |
| 0.0           | 32.0  | 7200  | 1.1042          | 0.8917   |
| 0.0           | 33.0  | 7425  | 1.1208          | 0.8917   |
| 0.0           | 34.0  | 7650  | 1.1187          | 0.89     |
| 0.0           | 35.0  | 7875  | 1.1334          | 0.89     |
| 0.0           | 36.0  | 8100  | 1.1314          | 0.8883   |
| 0.0           | 37.0  | 8325  | 1.1340          | 0.89     |
| 0.0           | 38.0  | 8550  | 1.1418          | 0.8883   |
| 0.0           | 39.0  | 8775  | 1.1558          | 0.8883   |
| 0.0           | 40.0  | 9000  | 1.1604          | 0.89     |
| 0.0           | 41.0  | 9225  | 1.1596          | 0.8867   |
| 0.0028        | 42.0  | 9450  | 1.1661          | 0.8867   |
| 0.0           | 43.0  | 9675  | 1.1764          | 0.8883   |
| 0.0026        | 44.0  | 9900  | 1.1730          | 0.8867   |
| 0.0           | 45.0  | 10125 | 1.1807          | 0.89     |
| 0.0           | 46.0  | 10350 | 1.1827          | 0.8883   |
| 0.0           | 47.0  | 10575 | 1.1851          | 0.89     |
| 0.0           | 48.0  | 10800 | 1.1866          | 0.89     |
| 0.0           | 49.0  | 11025 | 1.1856          | 0.8883   |
| 0.0           | 50.0  | 11250 | 1.1878          | 0.89     |


### 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