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# smids_3x_deit_tiny_adamax_0001_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: 1.0425
- Accuracy: 0.8769

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3075        | 1.0   | 225   | 0.3475          | 0.8502   |
| 0.1874        | 2.0   | 450   | 0.4269          | 0.8519   |
| 0.1627        | 3.0   | 675   | 0.3974          | 0.8652   |
| 0.1048        | 4.0   | 900   | 0.4256          | 0.8669   |
| 0.0866        | 5.0   | 1125  | 0.5024          | 0.8686   |
| 0.0482        | 6.0   | 1350  | 0.5673          | 0.8752   |
| 0.0764        | 7.0   | 1575  | 0.8107          | 0.8652   |
| 0.0186        | 8.0   | 1800  | 0.7251          | 0.8885   |
| 0.0334        | 9.0   | 2025  | 0.7854          | 0.8686   |
| 0.0009        | 10.0  | 2250  | 0.8924          | 0.8619   |
| 0.0392        | 11.0  | 2475  | 0.8255          | 0.8686   |
| 0.0005        | 12.0  | 2700  | 0.9410          | 0.8686   |
| 0.0101        | 13.0  | 2925  | 0.9076          | 0.8702   |
| 0.0133        | 14.0  | 3150  | 1.0016          | 0.8602   |
| 0.0           | 15.0  | 3375  | 0.8996          | 0.8752   |
| 0.0001        | 16.0  | 3600  | 0.9808          | 0.8636   |
| 0.0141        | 17.0  | 3825  | 1.0333          | 0.8586   |
| 0.0001        | 18.0  | 4050  | 1.0084          | 0.8686   |
| 0.0101        | 19.0  | 4275  | 0.9333          | 0.8769   |
| 0.0           | 20.0  | 4500  | 0.9573          | 0.8752   |
| 0.0           | 21.0  | 4725  | 0.9496          | 0.8819   |
| 0.0           | 22.0  | 4950  | 0.9897          | 0.8785   |
| 0.0057        | 23.0  | 5175  | 1.0229          | 0.8719   |
| 0.0           | 24.0  | 5400  | 0.9747          | 0.8785   |
| 0.0089        | 25.0  | 5625  | 1.1564          | 0.8602   |
| 0.0065        | 26.0  | 5850  | 1.0567          | 0.8735   |
| 0.0           | 27.0  | 6075  | 1.0448          | 0.8686   |
| 0.0           | 28.0  | 6300  | 1.0218          | 0.8769   |
| 0.0           | 29.0  | 6525  | 1.0556          | 0.8735   |
| 0.007         | 30.0  | 6750  | 0.9967          | 0.8719   |
| 0.0           | 31.0  | 6975  | 1.0179          | 0.8769   |
| 0.0           | 32.0  | 7200  | 1.0202          | 0.8752   |
| 0.0           | 33.0  | 7425  | 1.0274          | 0.8735   |
| 0.0           | 34.0  | 7650  | 1.0303          | 0.8669   |
| 0.0           | 35.0  | 7875  | 1.0234          | 0.8686   |
| 0.0038        | 36.0  | 8100  | 1.0313          | 0.8702   |
| 0.0           | 37.0  | 8325  | 1.0205          | 0.8735   |
| 0.0           | 38.0  | 8550  | 1.0300          | 0.8702   |
| 0.0041        | 39.0  | 8775  | 1.0336          | 0.8752   |
| 0.0           | 40.0  | 9000  | 1.0365          | 0.8735   |
| 0.0           | 41.0  | 9225  | 1.0406          | 0.8735   |
| 0.0           | 42.0  | 9450  | 1.0330          | 0.8752   |
| 0.0           | 43.0  | 9675  | 1.0322          | 0.8752   |
| 0.0           | 44.0  | 9900  | 1.0369          | 0.8752   |
| 0.0           | 45.0  | 10125 | 1.0431          | 0.8769   |
| 0.0           | 46.0  | 10350 | 1.0388          | 0.8752   |
| 0.0           | 47.0  | 10575 | 1.0369          | 0.8785   |
| 0.0           | 48.0  | 10800 | 1.0413          | 0.8769   |
| 0.0025        | 49.0  | 11025 | 1.0423          | 0.8769   |
| 0.0025        | 50.0  | 11250 | 1.0425          | 0.8769   |


### 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