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# smids_3x_deit_base_adamax_0001_fold5

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: 0.8955
- Accuracy: 0.895

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3014        | 1.0   | 225   | 0.3213          | 0.8717   |
| 0.0815        | 2.0   | 450   | 0.2900          | 0.8883   |
| 0.0668        | 3.0   | 675   | 0.4157          | 0.8817   |
| 0.0616        | 4.0   | 900   | 0.5373          | 0.8667   |
| 0.0313        | 5.0   | 1125  | 0.6762          | 0.8783   |
| 0.0139        | 6.0   | 1350  | 0.5421          | 0.8867   |
| 0.0228        | 7.0   | 1575  | 0.5956          | 0.885    |
| 0.0003        | 8.0   | 1800  | 0.6382          | 0.89     |
| 0.0191        | 9.0   | 2025  | 0.5798          | 0.8967   |
| 0.0066        | 10.0  | 2250  | 0.6950          | 0.8817   |
| 0.0002        | 11.0  | 2475  | 0.6959          | 0.8783   |
| 0.0105        | 12.0  | 2700  | 0.7866          | 0.8733   |
| 0.0009        | 13.0  | 2925  | 0.7496          | 0.8833   |
| 0.02          | 14.0  | 3150  | 0.7895          | 0.8917   |
| 0.0           | 15.0  | 3375  | 0.7885          | 0.8867   |
| 0.0           | 16.0  | 3600  | 0.8188          | 0.8783   |
| 0.0001        | 17.0  | 3825  | 0.8229          | 0.895    |
| 0.013         | 18.0  | 4050  | 0.8881          | 0.8867   |
| 0.008         | 19.0  | 4275  | 0.8377          | 0.8933   |
| 0.0001        | 20.0  | 4500  | 0.8361          | 0.8833   |
| 0.0           | 21.0  | 4725  | 0.8087          | 0.89     |
| 0.0           | 22.0  | 4950  | 0.8001          | 0.895    |
| 0.0           | 23.0  | 5175  | 0.7918          | 0.8933   |
| 0.0043        | 24.0  | 5400  | 0.8030          | 0.8917   |
| 0.0           | 25.0  | 5625  | 0.8092          | 0.895    |
| 0.0           | 26.0  | 5850  | 0.8142          | 0.8917   |
| 0.0           | 27.0  | 6075  | 0.8480          | 0.8833   |
| 0.0           | 28.0  | 6300  | 0.8065          | 0.8967   |
| 0.0035        | 29.0  | 6525  | 0.8102          | 0.8933   |
| 0.0           | 30.0  | 6750  | 0.8694          | 0.8933   |
| 0.0           | 31.0  | 6975  | 0.8371          | 0.8917   |
| 0.0           | 32.0  | 7200  | 0.8420          | 0.8933   |
| 0.0031        | 33.0  | 7425  | 0.8419          | 0.8933   |
| 0.0           | 34.0  | 7650  | 0.8459          | 0.8933   |
| 0.0028        | 35.0  | 7875  | 0.8578          | 0.8967   |
| 0.0           | 36.0  | 8100  | 0.8632          | 0.8917   |
| 0.0032        | 37.0  | 8325  | 0.8626          | 0.8933   |
| 0.0           | 38.0  | 8550  | 0.8689          | 0.895    |
| 0.0           | 39.0  | 8775  | 0.8751          | 0.8933   |
| 0.0           | 40.0  | 9000  | 0.8742          | 0.8933   |
| 0.0           | 41.0  | 9225  | 0.8784          | 0.8933   |
| 0.0           | 42.0  | 9450  | 0.8817          | 0.8933   |
| 0.0025        | 43.0  | 9675  | 0.8831          | 0.8933   |
| 0.0           | 44.0  | 9900  | 0.8843          | 0.895    |
| 0.0           | 45.0  | 10125 | 0.8846          | 0.895    |
| 0.0           | 46.0  | 10350 | 0.8903          | 0.895    |
| 0.0           | 47.0  | 10575 | 0.8928          | 0.895    |
| 0.0           | 48.0  | 10800 | 0.8941          | 0.895    |
| 0.0           | 49.0  | 11025 | 0.8949          | 0.895    |
| 0.0           | 50.0  | 11250 | 0.8955          | 0.895    |


### 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