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# smids_3x_deit_tiny_adamax_0001_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: 0.8277
- 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3246        | 1.0   | 225   | 0.2699          | 0.9017   |
| 0.2407        | 2.0   | 450   | 0.2916          | 0.8967   |
| 0.1215        | 3.0   | 675   | 0.3070          | 0.9      |
| 0.0685        | 4.0   | 900   | 0.3909          | 0.885    |
| 0.0669        | 5.0   | 1125  | 0.4441          | 0.8783   |
| 0.0554        | 6.0   | 1350  | 0.5807          | 0.9017   |
| 0.0636        | 7.0   | 1575  | 0.6509          | 0.8967   |
| 0.1012        | 8.0   | 1800  | 0.5814          | 0.9017   |
| 0.0299        | 9.0   | 2025  | 0.6645          | 0.885    |
| 0.0537        | 10.0  | 2250  | 0.7621          | 0.8817   |
| 0.0367        | 11.0  | 2475  | 0.7288          | 0.8983   |
| 0.0022        | 12.0  | 2700  | 0.7692          | 0.8883   |
| 0.0254        | 13.0  | 2925  | 0.7258          | 0.9017   |
| 0.002         | 14.0  | 3150  | 0.7885          | 0.895    |
| 0.0002        | 15.0  | 3375  | 0.8594          | 0.8833   |
| 0.0001        | 16.0  | 3600  | 0.9131          | 0.8767   |
| 0.0002        | 17.0  | 3825  | 0.8422          | 0.895    |
| 0.0           | 18.0  | 4050  | 0.8469          | 0.89     |
| 0.0           | 19.0  | 4275  | 0.8455          | 0.895    |
| 0.0           | 20.0  | 4500  | 0.8023          | 0.8967   |
| 0.0           | 21.0  | 4725  | 0.7909          | 0.895    |
| 0.0           | 22.0  | 4950  | 0.8061          | 0.8933   |
| 0.0           | 23.0  | 5175  | 0.7917          | 0.8983   |
| 0.0           | 24.0  | 5400  | 0.8680          | 0.8917   |
| 0.0117        | 25.0  | 5625  | 0.8057          | 0.89     |
| 0.0           | 26.0  | 5850  | 0.7951          | 0.9      |
| 0.0           | 27.0  | 6075  | 0.7910          | 0.8983   |
| 0.0           | 28.0  | 6300  | 0.8032          | 0.8917   |
| 0.005         | 29.0  | 6525  | 0.7940          | 0.8933   |
| 0.0052        | 30.0  | 6750  | 0.8144          | 0.8933   |
| 0.0044        | 31.0  | 6975  | 0.8155          | 0.895    |
| 0.0           | 32.0  | 7200  | 0.8385          | 0.8983   |
| 0.0           | 33.0  | 7425  | 0.8031          | 0.8983   |
| 0.0           | 34.0  | 7650  | 0.7944          | 0.9      |
| 0.0           | 35.0  | 7875  | 0.8238          | 0.895    |
| 0.0           | 36.0  | 8100  | 0.8091          | 0.8933   |
| 0.0           | 37.0  | 8325  | 0.8071          | 0.8967   |
| 0.0           | 38.0  | 8550  | 0.8231          | 0.895    |
| 0.0           | 39.0  | 8775  | 0.8187          | 0.895    |
| 0.0           | 40.0  | 9000  | 0.8174          | 0.895    |
| 0.0           | 41.0  | 9225  | 0.8091          | 0.8967   |
| 0.0           | 42.0  | 9450  | 0.8169          | 0.895    |
| 0.0           | 43.0  | 9675  | 0.8228          | 0.895    |
| 0.0           | 44.0  | 9900  | 0.8259          | 0.8933   |
| 0.0           | 45.0  | 10125 | 0.8259          | 0.895    |
| 0.0           | 46.0  | 10350 | 0.8242          | 0.895    |
| 0.0026        | 47.0  | 10575 | 0.8264          | 0.895    |
| 0.0           | 48.0  | 10800 | 0.8265          | 0.895    |
| 0.0           | 49.0  | 11025 | 0.8271          | 0.895    |
| 0.0           | 50.0  | 11250 | 0.8277          | 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