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# smids_3x_deit_base_adamax_0001_fold4

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2122
- Accuracy: 0.8817

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3352        | 1.0   | 225   | 0.3379          | 0.8717   |
| 0.1377        | 2.0   | 450   | 0.3835          | 0.8783   |
| 0.0291        | 3.0   | 675   | 0.5097          | 0.8667   |
| 0.0251        | 4.0   | 900   | 0.6529          | 0.8717   |
| 0.0357        | 5.0   | 1125  | 0.7618          | 0.86     |
| 0.0039        | 6.0   | 1350  | 0.6649          | 0.8767   |
| 0.0097        | 7.0   | 1575  | 0.6983          | 0.885    |
| 0.0025        | 8.0   | 1800  | 0.8127          | 0.8817   |
| 0.0074        | 9.0   | 2025  | 0.8386          | 0.8783   |
| 0.0002        | 10.0  | 2250  | 0.9901          | 0.875    |
| 0.0           | 11.0  | 2475  | 0.9453          | 0.8767   |
| 0.0           | 12.0  | 2700  | 0.9238          | 0.885    |
| 0.0           | 13.0  | 2925  | 0.9330          | 0.8867   |
| 0.0           | 14.0  | 3150  | 0.9456          | 0.8867   |
| 0.0           | 15.0  | 3375  | 0.9544          | 0.8833   |
| 0.0           | 16.0  | 3600  | 0.9815          | 0.8817   |
| 0.0           | 17.0  | 3825  | 0.9908          | 0.8783   |
| 0.0           | 18.0  | 4050  | 0.9927          | 0.8783   |
| 0.0           | 19.0  | 4275  | 0.9760          | 0.8817   |
| 0.0           | 20.0  | 4500  | 1.0014          | 0.8867   |
| 0.0           | 21.0  | 4725  | 1.0281          | 0.875    |
| 0.0           | 22.0  | 4950  | 1.0184          | 0.885    |
| 0.0           | 23.0  | 5175  | 1.0258          | 0.8817   |
| 0.0           | 24.0  | 5400  | 1.0403          | 0.8867   |
| 0.0           | 25.0  | 5625  | 1.0551          | 0.88     |
| 0.0           | 26.0  | 5850  | 1.0757          | 0.88     |
| 0.0           | 27.0  | 6075  | 1.0799          | 0.88     |
| 0.0           | 28.0  | 6300  | 1.0810          | 0.885    |
| 0.0           | 29.0  | 6525  | 1.0818          | 0.885    |
| 0.0           | 30.0  | 6750  | 1.0923          | 0.885    |
| 0.0           | 31.0  | 6975  | 1.1022          | 0.8833   |
| 0.0           | 32.0  | 7200  | 1.1086          | 0.8833   |
| 0.0           | 33.0  | 7425  | 1.1266          | 0.8833   |
| 0.0           | 34.0  | 7650  | 1.1235          | 0.8817   |
| 0.0           | 35.0  | 7875  | 1.1462          | 0.8817   |
| 0.0           | 36.0  | 8100  | 1.1417          | 0.8833   |
| 0.0           | 37.0  | 8325  | 1.1462          | 0.8817   |
| 0.0           | 38.0  | 8550  | 1.1576          | 0.88     |
| 0.0           | 39.0  | 8775  | 1.1622          | 0.8833   |
| 0.0           | 40.0  | 9000  | 1.1712          | 0.885    |
| 0.0           | 41.0  | 9225  | 1.1792          | 0.88     |
| 0.0027        | 42.0  | 9450  | 1.1851          | 0.8817   |
| 0.0           | 43.0  | 9675  | 1.1909          | 0.8833   |
| 0.0026        | 44.0  | 9900  | 1.1963          | 0.8817   |
| 0.0           | 45.0  | 10125 | 1.2008          | 0.8833   |
| 0.0           | 46.0  | 10350 | 1.2045          | 0.8817   |
| 0.0           | 47.0  | 10575 | 1.2080          | 0.8817   |
| 0.0           | 48.0  | 10800 | 1.2100          | 0.8817   |
| 0.0           | 49.0  | 11025 | 1.2116          | 0.8817   |
| 0.0           | 50.0  | 11250 | 1.2122          | 0.8817   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm