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# smids_3x_deit_tiny_adamax_0001_fold5

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: 0.8810
- Accuracy: 0.8933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3187        | 1.0   | 225   | 0.2926          | 0.8683   |
| 0.1675        | 2.0   | 450   | 0.2434          | 0.9083   |
| 0.148         | 3.0   | 675   | 0.3249          | 0.8883   |
| 0.0849        | 4.0   | 900   | 0.2613          | 0.91     |
| 0.0858        | 5.0   | 1125  | 0.4160          | 0.895    |
| 0.0448        | 6.0   | 1350  | 0.5146          | 0.8833   |
| 0.0473        | 7.0   | 1575  | 0.6012          | 0.8833   |
| 0.0246        | 8.0   | 1800  | 0.5599          | 0.89     |
| 0.0437        | 9.0   | 2025  | 0.6206          | 0.8933   |
| 0.0184        | 10.0  | 2250  | 0.6714          | 0.9017   |
| 0.0009        | 11.0  | 2475  | 0.6631          | 0.9083   |
| 0.0431        | 12.0  | 2700  | 0.7764          | 0.8983   |
| 0.0005        | 13.0  | 2925  | 0.6164          | 0.9033   |
| 0.0002        | 14.0  | 3150  | 0.6308          | 0.9017   |
| 0.0025        | 15.0  | 3375  | 0.7289          | 0.8983   |
| 0.0001        | 16.0  | 3600  | 0.6634          | 0.905    |
| 0.0           | 17.0  | 3825  | 0.7636          | 0.9033   |
| 0.0233        | 18.0  | 4050  | 0.7494          | 0.905    |
| 0.027         | 19.0  | 4275  | 0.8179          | 0.8917   |
| 0.0           | 20.0  | 4500  | 0.8300          | 0.895    |
| 0.0           | 21.0  | 4725  | 0.8262          | 0.8967   |
| 0.0           | 22.0  | 4950  | 0.8472          | 0.8917   |
| 0.0           | 23.0  | 5175  | 0.7368          | 0.9067   |
| 0.0091        | 24.0  | 5400  | 0.7922          | 0.8983   |
| 0.0           | 25.0  | 5625  | 0.8707          | 0.9      |
| 0.0           | 26.0  | 5850  | 0.7645          | 0.9017   |
| 0.0           | 27.0  | 6075  | 0.8363          | 0.8983   |
| 0.0           | 28.0  | 6300  | 0.8125          | 0.905    |
| 0.0076        | 29.0  | 6525  | 0.7853          | 0.9067   |
| 0.0           | 30.0  | 6750  | 0.8267          | 0.8967   |
| 0.0           | 31.0  | 6975  | 0.8018          | 0.905    |
| 0.0           | 32.0  | 7200  | 0.8256          | 0.9      |
| 0.0059        | 33.0  | 7425  | 0.8776          | 0.8967   |
| 0.0           | 34.0  | 7650  | 0.8060          | 0.9      |
| 0.0032        | 35.0  | 7875  | 0.8635          | 0.9      |
| 0.0           | 36.0  | 8100  | 0.8389          | 0.895    |
| 0.005         | 37.0  | 8325  | 0.8643          | 0.895    |
| 0.0           | 38.0  | 8550  | 0.8458          | 0.8983   |
| 0.0           | 39.0  | 8775  | 0.8735          | 0.895    |
| 0.0           | 40.0  | 9000  | 0.8584          | 0.895    |
| 0.0           | 41.0  | 9225  | 0.8812          | 0.895    |
| 0.0           | 42.0  | 9450  | 0.8710          | 0.895    |
| 0.0032        | 43.0  | 9675  | 0.8774          | 0.895    |
| 0.0           | 44.0  | 9900  | 0.8731          | 0.895    |
| 0.0           | 45.0  | 10125 | 0.8725          | 0.8967   |
| 0.0           | 46.0  | 10350 | 0.8786          | 0.895    |
| 0.0           | 47.0  | 10575 | 0.8795          | 0.895    |
| 0.0           | 48.0  | 10800 | 0.8801          | 0.895    |
| 0.0           | 49.0  | 11025 | 0.8803          | 0.8933   |
| 0.0           | 50.0  | 11250 | 0.8810          | 0.8933   |


### 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