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# smids_3x_deit_tiny_adamax_001_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: 0.9522
- Accuracy: 0.8935

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5261        | 1.0   | 225   | 0.3629          | 0.8552   |
| 0.3081        | 2.0   | 450   | 0.3850          | 0.8303   |
| 0.2433        | 3.0   | 675   | 0.4084          | 0.8486   |
| 0.2976        | 4.0   | 900   | 0.3348          | 0.8752   |
| 0.2892        | 5.0   | 1125  | 0.3154          | 0.8752   |
| 0.1338        | 6.0   | 1350  | 0.4213          | 0.8586   |
| 0.1811        | 7.0   | 1575  | 0.4568          | 0.8602   |
| 0.1262        | 8.0   | 1800  | 0.4156          | 0.8702   |
| 0.1405        | 9.0   | 2025  | 0.4962          | 0.8552   |
| 0.1378        | 10.0  | 2250  | 0.4880          | 0.8652   |
| 0.0783        | 11.0  | 2475  | 0.5529          | 0.8602   |
| 0.1156        | 12.0  | 2700  | 0.5059          | 0.8569   |
| 0.0435        | 13.0  | 2925  | 0.5510          | 0.8735   |
| 0.06          | 14.0  | 3150  | 0.5625          | 0.8669   |
| 0.0749        | 15.0  | 3375  | 0.6173          | 0.8719   |
| 0.0723        | 16.0  | 3600  | 0.5869          | 0.8785   |
| 0.0343        | 17.0  | 3825  | 0.6758          | 0.8852   |
| 0.0074        | 18.0  | 4050  | 0.7248          | 0.8686   |
| 0.0351        | 19.0  | 4275  | 0.6545          | 0.8785   |
| 0.0367        | 20.0  | 4500  | 0.7634          | 0.8785   |
| 0.0039        | 21.0  | 4725  | 0.8073          | 0.8752   |
| 0.0183        | 22.0  | 4950  | 0.6969          | 0.8869   |
| 0.015         | 23.0  | 5175  | 0.7193          | 0.8885   |
| 0.0003        | 24.0  | 5400  | 0.8406          | 0.8719   |
| 0.0461        | 25.0  | 5625  | 0.8687          | 0.8702   |
| 0.0004        | 26.0  | 5850  | 0.7424          | 0.8802   |
| 0.0001        | 27.0  | 6075  | 0.8481          | 0.8819   |
| 0.0001        | 28.0  | 6300  | 0.8060          | 0.8785   |
| 0.0003        | 29.0  | 6525  | 0.8316          | 0.8869   |
| 0.0012        | 30.0  | 6750  | 0.8183          | 0.8835   |
| 0.007         | 31.0  | 6975  | 0.7519          | 0.8802   |
| 0.0           | 32.0  | 7200  | 0.8429          | 0.8852   |
| 0.002         | 33.0  | 7425  | 0.8340          | 0.8885   |
| 0.0           | 34.0  | 7650  | 0.8626          | 0.8785   |
| 0.0           | 35.0  | 7875  | 0.8155          | 0.8935   |
| 0.0035        | 36.0  | 8100  | 0.8392          | 0.8918   |
| 0.0           | 37.0  | 8325  | 0.9154          | 0.8852   |
| 0.0           | 38.0  | 8550  | 0.9252          | 0.8885   |
| 0.0047        | 39.0  | 8775  | 0.9247          | 0.8852   |
| 0.0           | 40.0  | 9000  | 0.9286          | 0.8918   |
| 0.0           | 41.0  | 9225  | 0.9340          | 0.8902   |
| 0.0           | 42.0  | 9450  | 0.9212          | 0.8885   |
| 0.0           | 43.0  | 9675  | 0.9298          | 0.8902   |
| 0.0           | 44.0  | 9900  | 0.9334          | 0.8935   |
| 0.0           | 45.0  | 10125 | 0.9402          | 0.8952   |
| 0.0           | 46.0  | 10350 | 0.9378          | 0.8952   |
| 0.0           | 47.0  | 10575 | 0.9454          | 0.8918   |
| 0.0           | 48.0  | 10800 | 0.9493          | 0.8935   |
| 0.0024        | 49.0  | 11025 | 0.9513          | 0.8935   |
| 0.0024        | 50.0  | 11250 | 0.9522          | 0.8935   |


### 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