
<!-- 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_tiny_adamax_00001_fold4

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: 1.1449
- Accuracy: 0.8667

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4417        | 1.0   | 225   | 0.4569          | 0.8083   |
| 0.2331        | 2.0   | 450   | 0.3996          | 0.8383   |
| 0.2011        | 3.0   | 675   | 0.3738          | 0.8467   |
| 0.2185        | 4.0   | 900   | 0.3745          | 0.8633   |
| 0.1818        | 5.0   | 1125  | 0.3725          | 0.875    |
| 0.1063        | 6.0   | 1350  | 0.4112          | 0.8717   |
| 0.1356        | 7.0   | 1575  | 0.4194          | 0.875    |
| 0.1607        | 8.0   | 1800  | 0.4543          | 0.8533   |
| 0.1116        | 9.0   | 2025  | 0.4653          | 0.8683   |
| 0.0765        | 10.0  | 2250  | 0.5277          | 0.8733   |
| 0.0939        | 11.0  | 2475  | 0.5314          | 0.875    |
| 0.0447        | 12.0  | 2700  | 0.6029          | 0.87     |
| 0.0241        | 13.0  | 2925  | 0.6080          | 0.88     |
| 0.0181        | 14.0  | 3150  | 0.6484          | 0.8783   |
| 0.0217        | 15.0  | 3375  | 0.7384          | 0.8733   |
| 0.0012        | 16.0  | 3600  | 0.7227          | 0.8717   |
| 0.0028        | 17.0  | 3825  | 0.7801          | 0.8683   |
| 0.0175        | 18.0  | 4050  | 0.7970          | 0.8667   |
| 0.0003        | 19.0  | 4275  | 0.8483          | 0.8683   |
| 0.0027        | 20.0  | 4500  | 0.8736          | 0.875    |
| 0.0021        | 21.0  | 4725  | 0.8846          | 0.8717   |
| 0.0002        | 22.0  | 4950  | 0.8988          | 0.875    |
| 0.0002        | 23.0  | 5175  | 0.9264          | 0.8683   |
| 0.0001        | 24.0  | 5400  | 0.9315          | 0.8717   |
| 0.0001        | 25.0  | 5625  | 0.9598          | 0.87     |
| 0.0001        | 26.0  | 5850  | 0.9683          | 0.8667   |
| 0.0001        | 27.0  | 6075  | 0.9914          | 0.8667   |
| 0.0           | 28.0  | 6300  | 1.0058          | 0.8683   |
| 0.0001        | 29.0  | 6525  | 1.0095          | 0.8683   |
| 0.0           | 30.0  | 6750  | 1.0176          | 0.865    |
| 0.0           | 31.0  | 6975  | 1.0268          | 0.865    |
| 0.0           | 32.0  | 7200  | 1.0316          | 0.8667   |
| 0.0           | 33.0  | 7425  | 1.0500          | 0.8667   |
| 0.0           | 34.0  | 7650  | 1.0562          | 0.87     |
| 0.0           | 35.0  | 7875  | 1.0705          | 0.8667   |
| 0.0           | 36.0  | 8100  | 1.0794          | 0.8683   |
| 0.0           | 37.0  | 8325  | 1.0868          | 0.8667   |
| 0.0           | 38.0  | 8550  | 1.0946          | 0.865    |
| 0.0           | 39.0  | 8775  | 1.1049          | 0.8667   |
| 0.0           | 40.0  | 9000  | 1.1087          | 0.8667   |
| 0.0           | 41.0  | 9225  | 1.1122          | 0.8667   |
| 0.0091        | 42.0  | 9450  | 1.1194          | 0.8667   |
| 0.0           | 43.0  | 9675  | 1.1272          | 0.8667   |
| 0.0077        | 44.0  | 9900  | 1.1280          | 0.8667   |
| 0.0           | 45.0  | 10125 | 1.1353          | 0.8667   |
| 0.0           | 46.0  | 10350 | 1.1386          | 0.8667   |
| 0.0           | 47.0  | 10575 | 1.1418          | 0.8683   |
| 0.0           | 48.0  | 10800 | 1.1440          | 0.8667   |
| 0.0           | 49.0  | 11025 | 1.1434          | 0.8667   |
| 0.0           | 50.0  | 11250 | 1.1449          | 0.8667   |


### 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