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# smids_3x_deit_base_adamax_00001_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: 0.6560
- Accuracy: 0.9017

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3528        | 1.0   | 225   | 0.3515          | 0.845    |
| 0.2402        | 2.0   | 450   | 0.2867          | 0.88     |
| 0.1984        | 3.0   | 675   | 0.2729          | 0.8917   |
| 0.0996        | 4.0   | 900   | 0.2824          | 0.8917   |
| 0.0553        | 5.0   | 1125  | 0.2965          | 0.89     |
| 0.0276        | 6.0   | 1350  | 0.3082          | 0.8833   |
| 0.0633        | 7.0   | 1575  | 0.3636          | 0.8917   |
| 0.0425        | 8.0   | 1800  | 0.3934          | 0.8917   |
| 0.0389        | 9.0   | 2025  | 0.4319          | 0.8867   |
| 0.0013        | 10.0  | 2250  | 0.4665          | 0.885    |
| 0.002         | 11.0  | 2475  | 0.4939          | 0.89     |
| 0.0291        | 12.0  | 2700  | 0.5069          | 0.8983   |
| 0.0096        | 13.0  | 2925  | 0.5116          | 0.8967   |
| 0.0004        | 14.0  | 3150  | 0.5365          | 0.89     |
| 0.0003        | 15.0  | 3375  | 0.5451          | 0.8933   |
| 0.0001        | 16.0  | 3600  | 0.5523          | 0.9017   |
| 0.0001        | 17.0  | 3825  | 0.5663          | 0.895    |
| 0.0002        | 18.0  | 4050  | 0.6978          | 0.88     |
| 0.0158        | 19.0  | 4275  | 0.5838          | 0.8983   |
| 0.0001        | 20.0  | 4500  | 0.6267          | 0.8917   |
| 0.0001        | 21.0  | 4725  | 0.6223          | 0.8833   |
| 0.0001        | 22.0  | 4950  | 0.5930          | 0.8983   |
| 0.0001        | 23.0  | 5175  | 0.6068          | 0.8967   |
| 0.01          | 24.0  | 5400  | 0.5979          | 0.895    |
| 0.0001        | 25.0  | 5625  | 0.6216          | 0.885    |
| 0.0001        | 26.0  | 5850  | 0.6186          | 0.89     |
| 0.0001        | 27.0  | 6075  | 0.6718          | 0.88     |
| 0.0001        | 28.0  | 6300  | 0.6180          | 0.8917   |
| 0.0037        | 29.0  | 6525  | 0.6199          | 0.895    |
| 0.0           | 30.0  | 6750  | 0.6476          | 0.8933   |
| 0.0           | 31.0  | 6975  | 0.6341          | 0.8933   |
| 0.0           | 32.0  | 7200  | 0.6370          | 0.895    |
| 0.0032        | 33.0  | 7425  | 0.6400          | 0.895    |
| 0.0           | 34.0  | 7650  | 0.6373          | 0.8933   |
| 0.0026        | 35.0  | 7875  | 0.6425          | 0.8967   |
| 0.0           | 36.0  | 8100  | 0.6390          | 0.8983   |
| 0.0053        | 37.0  | 8325  | 0.6460          | 0.895    |
| 0.0           | 38.0  | 8550  | 0.6514          | 0.8933   |
| 0.0           | 39.0  | 8775  | 0.6474          | 0.8983   |
| 0.0           | 40.0  | 9000  | 0.6511          | 0.8933   |
| 0.0           | 41.0  | 9225  | 0.6568          | 0.8933   |
| 0.0           | 42.0  | 9450  | 0.6574          | 0.8933   |
| 0.0026        | 43.0  | 9675  | 0.6562          | 0.8967   |
| 0.0           | 44.0  | 9900  | 0.6548          | 0.8983   |
| 0.0           | 45.0  | 10125 | 0.6524          | 0.895    |
| 0.0           | 46.0  | 10350 | 0.6552          | 0.8983   |
| 0.0           | 47.0  | 10575 | 0.6559          | 0.8983   |
| 0.0           | 48.0  | 10800 | 0.6560          | 0.8983   |
| 0.0           | 49.0  | 11025 | 0.6556          | 0.9017   |
| 0.0           | 50.0  | 11250 | 0.6560          | 0.9017   |


### 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