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# smids_10x_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: 0.8747
- Accuracy: 0.92

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1226        | 1.0   | 750   | 0.2826          | 0.89     |
| 0.1           | 2.0   | 1500  | 0.3891          | 0.9017   |
| 0.0601        | 3.0   | 2250  | 0.4750          | 0.9133   |
| 0.0263        | 4.0   | 3000  | 0.4903          | 0.9117   |
| 0.0141        | 5.0   | 3750  | 0.6938          | 0.905    |
| 0.0263        | 6.0   | 4500  | 0.7445          | 0.895    |
| 0.0152        | 7.0   | 5250  | 0.5895          | 0.92     |
| 0.0017        | 8.0   | 6000  | 0.6688          | 0.92     |
| 0.0043        | 9.0   | 6750  | 0.6499          | 0.9167   |
| 0.0           | 10.0  | 7500  | 0.7976          | 0.91     |
| 0.011         | 11.0  | 8250  | 0.6899          | 0.9183   |
| 0.0           | 12.0  | 9000  | 0.6947          | 0.905    |
| 0.0           | 13.0  | 9750  | 0.8213          | 0.9067   |
| 0.0           | 14.0  | 10500 | 0.6853          | 0.9217   |
| 0.0           | 15.0  | 11250 | 0.8220          | 0.9117   |
| 0.0           | 16.0  | 12000 | 0.6897          | 0.9217   |
| 0.0038        | 17.0  | 12750 | 0.7869          | 0.9167   |
| 0.0004        | 18.0  | 13500 | 0.7956          | 0.9233   |
| 0.0031        | 19.0  | 14250 | 0.8745          | 0.9067   |
| 0.0           | 20.0  | 15000 | 0.7439          | 0.92     |
| 0.0           | 21.0  | 15750 | 0.7389          | 0.9233   |
| 0.0           | 22.0  | 16500 | 0.7135          | 0.925    |
| 0.0028        | 23.0  | 17250 | 0.7301          | 0.925    |
| 0.0           | 24.0  | 18000 | 0.7434          | 0.9283   |
| 0.0           | 25.0  | 18750 | 0.7538          | 0.9233   |
| 0.0           | 26.0  | 19500 | 0.7620          | 0.9233   |
| 0.0           | 27.0  | 20250 | 0.7318          | 0.9233   |
| 0.0           | 28.0  | 21000 | 0.7471          | 0.92     |
| 0.0           | 29.0  | 21750 | 0.7685          | 0.9183   |
| 0.0046        | 30.0  | 22500 | 0.8050          | 0.92     |
| 0.0           | 31.0  | 23250 | 0.7778          | 0.92     |
| 0.0           | 32.0  | 24000 | 0.7888          | 0.9217   |
| 0.0           | 33.0  | 24750 | 0.7725          | 0.9233   |
| 0.0           | 34.0  | 25500 | 0.8185          | 0.9167   |
| 0.0           | 35.0  | 26250 | 0.8187          | 0.92     |
| 0.0           | 36.0  | 27000 | 0.8276          | 0.92     |
| 0.0031        | 37.0  | 27750 | 0.8218          | 0.9217   |
| 0.0           | 38.0  | 28500 | 0.8408          | 0.92     |
| 0.0           | 39.0  | 29250 | 0.8462          | 0.9183   |
| 0.0           | 40.0  | 30000 | 0.8525          | 0.92     |
| 0.0           | 41.0  | 30750 | 0.8553          | 0.92     |
| 0.0           | 42.0  | 31500 | 0.8584          | 0.92     |
| 0.0           | 43.0  | 32250 | 0.8634          | 0.9183   |
| 0.0           | 44.0  | 33000 | 0.8639          | 0.92     |
| 0.0           | 45.0  | 33750 | 0.8671          | 0.92     |
| 0.0           | 46.0  | 34500 | 0.8704          | 0.92     |
| 0.0           | 47.0  | 35250 | 0.8722          | 0.92     |
| 0.0           | 48.0  | 36000 | 0.8729          | 0.92     |
| 0.0           | 49.0  | 36750 | 0.8728          | 0.92     |
| 0.0           | 50.0  | 37500 | 0.8747          | 0.92     |


### 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