
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_10x_deit_small_adamax_00001_fold4

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3483
- Accuracy: 0.8783

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2714        | 1.0   | 750   | 0.3483          | 0.875    |
| 0.1928        | 2.0   | 1500  | 0.3347          | 0.8833   |
| 0.1383        | 3.0   | 2250  | 0.3802          | 0.87     |
| 0.0835        | 4.0   | 3000  | 0.4083          | 0.8833   |
| 0.07          | 5.0   | 3750  | 0.4749          | 0.8833   |
| 0.0338        | 6.0   | 4500  | 0.5541          | 0.8767   |
| 0.0133        | 7.0   | 5250  | 0.6527          | 0.8783   |
| 0.0087        | 8.0   | 6000  | 0.7143          | 0.88     |
| 0.0145        | 9.0   | 6750  | 0.7738          | 0.88     |
| 0.0002        | 10.0  | 7500  | 0.8388          | 0.8767   |
| 0.0004        | 11.0  | 8250  | 0.9053          | 0.8817   |
| 0.0065        | 12.0  | 9000  | 0.9720          | 0.8783   |
| 0.0           | 13.0  | 9750  | 1.0304          | 0.8767   |
| 0.0           | 14.0  | 10500 | 1.0771          | 0.8717   |
| 0.0           | 15.0  | 11250 | 1.0764          | 0.8783   |
| 0.0326        | 16.0  | 12000 | 1.0955          | 0.8833   |
| 0.0001        | 17.0  | 12750 | 1.0921          | 0.8817   |
| 0.0           | 18.0  | 13500 | 1.1024          | 0.8817   |
| 0.0           | 19.0  | 14250 | 1.1225          | 0.8817   |
| 0.0           | 20.0  | 15000 | 1.1467          | 0.88     |
| 0.0           | 21.0  | 15750 | 1.1711          | 0.88     |
| 0.0           | 22.0  | 16500 | 1.1842          | 0.8783   |
| 0.0           | 23.0  | 17250 | 1.1878          | 0.8783   |
| 0.0           | 24.0  | 18000 | 1.2170          | 0.8817   |
| 0.0           | 25.0  | 18750 | 1.2183          | 0.88     |
| 0.0           | 26.0  | 19500 | 1.2367          | 0.88     |
| 0.0           | 27.0  | 20250 | 1.2535          | 0.8783   |
| 0.0           | 28.0  | 21000 | 1.2655          | 0.8833   |
| 0.0           | 29.0  | 21750 | 1.2701          | 0.8783   |
| 0.0           | 30.0  | 22500 | 1.2647          | 0.8783   |
| 0.0           | 31.0  | 23250 | 1.2884          | 0.8783   |
| 0.0           | 32.0  | 24000 | 1.2899          | 0.8733   |
| 0.0           | 33.0  | 24750 | 1.3073          | 0.8817   |
| 0.0           | 34.0  | 25500 | 1.3112          | 0.8833   |
| 0.0           | 35.0  | 26250 | 1.3094          | 0.8817   |
| 0.0           | 36.0  | 27000 | 1.3116          | 0.88     |
| 0.0           | 37.0  | 27750 | 1.3157          | 0.88     |
| 0.0           | 38.0  | 28500 | 1.3213          | 0.88     |
| 0.0           | 39.0  | 29250 | 1.3285          | 0.8767   |
| 0.0           | 40.0  | 30000 | 1.3297          | 0.8767   |
| 0.0           | 41.0  | 30750 | 1.3323          | 0.8783   |
| 0.0           | 42.0  | 31500 | 1.3346          | 0.8767   |
| 0.0           | 43.0  | 32250 | 1.3389          | 0.8783   |
| 0.0           | 44.0  | 33000 | 1.3404          | 0.8783   |
| 0.0           | 45.0  | 33750 | 1.3431          | 0.8783   |
| 0.0           | 46.0  | 34500 | 1.3453          | 0.8783   |
| 0.0           | 47.0  | 35250 | 1.3463          | 0.8783   |
| 0.0           | 48.0  | 36000 | 1.3478          | 0.8783   |
| 0.0           | 49.0  | 36750 | 1.3483          | 0.8783   |
| 0.0           | 50.0  | 37500 | 1.3483          | 0.8783   |


### 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