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# smids_3x_deit_base_adamax_00001_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: 0.9973
- Accuracy: 0.88

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3446        | 1.0   | 225   | 0.3635          | 0.845    |
| 0.1918        | 2.0   | 450   | 0.3231          | 0.8667   |
| 0.1259        | 3.0   | 675   | 0.3259          | 0.8767   |
| 0.1244        | 4.0   | 900   | 0.3347          | 0.8767   |
| 0.0882        | 5.0   | 1125  | 0.3474          | 0.875    |
| 0.0401        | 6.0   | 1350  | 0.4001          | 0.8767   |
| 0.0341        | 7.0   | 1575  | 0.4822          | 0.8717   |
| 0.0545        | 8.0   | 1800  | 0.4972          | 0.8783   |
| 0.0266        | 9.0   | 2025  | 0.6098          | 0.8633   |
| 0.0023        | 10.0  | 2250  | 0.5905          | 0.87     |
| 0.0071        | 11.0  | 2475  | 0.6351          | 0.8733   |
| 0.0009        | 12.0  | 2700  | 0.6746          | 0.8767   |
| 0.0003        | 13.0  | 2925  | 0.7065          | 0.8833   |
| 0.0003        | 14.0  | 3150  | 0.7353          | 0.8667   |
| 0.0002        | 15.0  | 3375  | 0.7491          | 0.8733   |
| 0.0001        | 16.0  | 3600  | 0.7500          | 0.8717   |
| 0.0001        | 17.0  | 3825  | 0.7514          | 0.8733   |
| 0.0001        | 18.0  | 4050  | 0.7869          | 0.8817   |
| 0.0001        | 19.0  | 4275  | 0.7968          | 0.8717   |
| 0.0001        | 20.0  | 4500  | 0.8160          | 0.8767   |
| 0.0           | 21.0  | 4725  | 0.8138          | 0.8783   |
| 0.0           | 22.0  | 4950  | 0.8558          | 0.87     |
| 0.0001        | 23.0  | 5175  | 0.8592          | 0.87     |
| 0.0           | 24.0  | 5400  | 0.8588          | 0.8717   |
| 0.0001        | 25.0  | 5625  | 0.8863          | 0.8767   |
| 0.0           | 26.0  | 5850  | 0.8795          | 0.88     |
| 0.0           | 27.0  | 6075  | 0.8854          | 0.8783   |
| 0.0           | 28.0  | 6300  | 0.8854          | 0.8767   |
| 0.0           | 29.0  | 6525  | 0.8995          | 0.875    |
| 0.0           | 30.0  | 6750  | 0.9074          | 0.8767   |
| 0.0           | 31.0  | 6975  | 0.9134          | 0.875    |
| 0.0           | 32.0  | 7200  | 0.9303          | 0.8767   |
| 0.0           | 33.0  | 7425  | 0.9263          | 0.875    |
| 0.0           | 34.0  | 7650  | 0.9402          | 0.8783   |
| 0.0           | 35.0  | 7875  | 0.9446          | 0.8783   |
| 0.0           | 36.0  | 8100  | 0.9522          | 0.88     |
| 0.0           | 37.0  | 8325  | 0.9608          | 0.8783   |
| 0.0           | 38.0  | 8550  | 0.9621          | 0.88     |
| 0.0           | 39.0  | 8775  | 0.9583          | 0.8733   |
| 0.0           | 40.0  | 9000  | 0.9729          | 0.8783   |
| 0.0           | 41.0  | 9225  | 0.9763          | 0.88     |
| 0.0026        | 42.0  | 9450  | 0.9780          | 0.88     |
| 0.0           | 43.0  | 9675  | 0.9802          | 0.8783   |
| 0.0024        | 44.0  | 9900  | 0.9867          | 0.88     |
| 0.0           | 45.0  | 10125 | 0.9887          | 0.8783   |
| 0.0           | 46.0  | 10350 | 0.9913          | 0.88     |
| 0.0           | 47.0  | 10575 | 0.9944          | 0.88     |
| 0.0           | 48.0  | 10800 | 0.9959          | 0.88     |
| 0.0           | 49.0  | 11025 | 0.9981          | 0.88     |
| 0.0           | 50.0  | 11250 | 0.9973          | 0.88     |


### 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