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# smids_10x_deit_tiny_adamax_00001_fold3

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: 0.8454
- Accuracy: 0.9117

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3334        | 1.0   | 750   | 0.3137          | 0.8867   |
| 0.2212        | 2.0   | 1500  | 0.2847          | 0.8933   |
| 0.1557        | 3.0   | 2250  | 0.2655          | 0.9017   |
| 0.1618        | 4.0   | 3000  | 0.2547          | 0.9083   |
| 0.1458        | 5.0   | 3750  | 0.2827          | 0.9117   |
| 0.0827        | 6.0   | 4500  | 0.3021          | 0.9067   |
| 0.0585        | 7.0   | 5250  | 0.3302          | 0.915    |
| 0.038         | 8.0   | 6000  | 0.3826          | 0.9117   |
| 0.0072        | 9.0   | 6750  | 0.4421          | 0.9033   |
| 0.0278        | 10.0  | 7500  | 0.4604          | 0.9117   |
| 0.024         | 11.0  | 8250  | 0.5200          | 0.9083   |
| 0.0385        | 12.0  | 9000  | 0.5950          | 0.905    |
| 0.0036        | 13.0  | 9750  | 0.6062          | 0.9167   |
| 0.0266        | 14.0  | 10500 | 0.6478          | 0.9083   |
| 0.0002        | 15.0  | 11250 | 0.6832          | 0.905    |
| 0.0008        | 16.0  | 12000 | 0.6882          | 0.9083   |
| 0.0001        | 17.0  | 12750 | 0.7251          | 0.9033   |
| 0.0001        | 18.0  | 13500 | 0.7412          | 0.9      |
| 0.0           | 19.0  | 14250 | 0.7303          | 0.905    |
| 0.0           | 20.0  | 15000 | 0.7516          | 0.9083   |
| 0.0           | 21.0  | 15750 | 0.7572          | 0.905    |
| 0.0           | 22.0  | 16500 | 0.7738          | 0.9033   |
| 0.0           | 23.0  | 17250 | 0.7783          | 0.905    |
| 0.0           | 24.0  | 18000 | 0.7887          | 0.9083   |
| 0.0           | 25.0  | 18750 | 0.8047          | 0.9067   |
| 0.0           | 26.0  | 19500 | 0.7947          | 0.905    |
| 0.0           | 27.0  | 20250 | 0.8074          | 0.9017   |
| 0.0           | 28.0  | 21000 | 0.8188          | 0.9067   |
| 0.0           | 29.0  | 21750 | 0.8171          | 0.9083   |
| 0.0           | 30.0  | 22500 | 0.8254          | 0.91     |
| 0.0           | 31.0  | 23250 | 0.8136          | 0.91     |
| 0.0           | 32.0  | 24000 | 0.8161          | 0.9117   |
| 0.0           | 33.0  | 24750 | 0.8510          | 0.9083   |
| 0.0           | 34.0  | 25500 | 0.8503          | 0.905    |
| 0.0           | 35.0  | 26250 | 0.8125          | 0.9083   |
| 0.0           | 36.0  | 27000 | 0.8406          | 0.9083   |
| 0.0           | 37.0  | 27750 | 0.8651          | 0.9033   |
| 0.0           | 38.0  | 28500 | 0.8378          | 0.9083   |
| 0.0           | 39.0  | 29250 | 0.8214          | 0.905    |
| 0.0           | 40.0  | 30000 | 0.8342          | 0.91     |
| 0.0           | 41.0  | 30750 | 0.8303          | 0.905    |
| 0.0           | 42.0  | 31500 | 0.8436          | 0.9117   |
| 0.0           | 43.0  | 32250 | 0.8482          | 0.9083   |
| 0.0           | 44.0  | 33000 | 0.8466          | 0.9117   |
| 0.0           | 45.0  | 33750 | 0.8396          | 0.9083   |
| 0.0           | 46.0  | 34500 | 0.8440          | 0.9117   |
| 0.0           | 47.0  | 35250 | 0.8439          | 0.9117   |
| 0.0           | 48.0  | 36000 | 0.8441          | 0.91     |
| 0.0           | 49.0  | 36750 | 0.8457          | 0.9117   |
| 0.0           | 50.0  | 37500 | 0.8454          | 0.9117   |


### 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