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# smids_3x_deit_tiny_adamax_00001_fold2

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: 0.9651
- Accuracy: 0.8719

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4234        | 1.0   | 225   | 0.4415          | 0.8186   |
| 0.3257        | 2.0   | 450   | 0.3946          | 0.8353   |
| 0.2484        | 3.0   | 675   | 0.3209          | 0.8719   |
| 0.2642        | 4.0   | 900   | 0.3296          | 0.8702   |
| 0.1771        | 5.0   | 1125  | 0.3172          | 0.8702   |
| 0.173         | 6.0   | 1350  | 0.3544          | 0.8586   |
| 0.1484        | 7.0   | 1575  | 0.3447          | 0.8702   |
| 0.0971        | 8.0   | 1800  | 0.3412          | 0.8785   |
| 0.1373        | 9.0   | 2025  | 0.3898          | 0.8735   |
| 0.0316        | 10.0  | 2250  | 0.4151          | 0.8702   |
| 0.056         | 11.0  | 2475  | 0.4368          | 0.8669   |
| 0.0751        | 12.0  | 2700  | 0.5052          | 0.8752   |
| 0.0299        | 13.0  | 2925  | 0.5143          | 0.8769   |
| 0.0139        | 14.0  | 3150  | 0.5498          | 0.8802   |
| 0.0304        | 15.0  | 3375  | 0.6069          | 0.8752   |
| 0.0339        | 16.0  | 3600  | 0.6246          | 0.8785   |
| 0.0114        | 17.0  | 3825  | 0.6695          | 0.8735   |
| 0.0005        | 18.0  | 4050  | 0.7207          | 0.8702   |
| 0.0014        | 19.0  | 4275  | 0.7338          | 0.8669   |
| 0.0073        | 20.0  | 4500  | 0.7686          | 0.8669   |
| 0.0003        | 21.0  | 4725  | 0.8099          | 0.8669   |
| 0.0003        | 22.0  | 4950  | 0.8291          | 0.8686   |
| 0.002         | 23.0  | 5175  | 0.8321          | 0.8669   |
| 0.0005        | 24.0  | 5400  | 0.8587          | 0.8669   |
| 0.0136        | 25.0  | 5625  | 0.8652          | 0.8702   |
| 0.0002        | 26.0  | 5850  | 0.8692          | 0.8702   |
| 0.0001        | 27.0  | 6075  | 0.8870          | 0.8702   |
| 0.0001        | 28.0  | 6300  | 0.8976          | 0.8686   |
| 0.0057        | 29.0  | 6525  | 0.9057          | 0.8719   |
| 0.0181        | 30.0  | 6750  | 0.9160          | 0.8719   |
| 0.0001        | 31.0  | 6975  | 0.9098          | 0.8702   |
| 0.0001        | 32.0  | 7200  | 0.9072          | 0.8719   |
| 0.0001        | 33.0  | 7425  | 0.9201          | 0.8686   |
| 0.0001        | 34.0  | 7650  | 0.9257          | 0.8752   |
| 0.0           | 35.0  | 7875  | 0.9399          | 0.8702   |
| 0.0059        | 36.0  | 8100  | 0.9356          | 0.8686   |
| 0.0           | 37.0  | 8325  | 0.9495          | 0.8719   |
| 0.0           | 38.0  | 8550  | 0.9431          | 0.8702   |
| 0.0039        | 39.0  | 8775  | 0.9538          | 0.8702   |
| 0.0           | 40.0  | 9000  | 0.9587          | 0.8686   |
| 0.0           | 41.0  | 9225  | 0.9526          | 0.8702   |
| 0.0           | 42.0  | 9450  | 0.9618          | 0.8686   |
| 0.0           | 43.0  | 9675  | 0.9656          | 0.8719   |
| 0.0           | 44.0  | 9900  | 0.9609          | 0.8719   |
| 0.0001        | 45.0  | 10125 | 0.9585          | 0.8719   |
| 0.0           | 46.0  | 10350 | 0.9641          | 0.8702   |
| 0.0           | 47.0  | 10575 | 0.9675          | 0.8686   |
| 0.0           | 48.0  | 10800 | 0.9660          | 0.8719   |
| 0.0045        | 49.0  | 11025 | 0.9652          | 0.8719   |
| 0.0044        | 50.0  | 11250 | 0.9651          | 0.8719   |


### 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