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# smids_10x_deit_small_adamax_001_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: 1.6552
- Accuracy: 0.8733

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2966        | 1.0   | 750   | 0.3694          | 0.8683   |
| 0.2339        | 2.0   | 1500  | 0.4363          | 0.8417   |
| 0.2355        | 3.0   | 2250  | 0.4010          | 0.8633   |
| 0.1515        | 4.0   | 3000  | 0.4419          | 0.8767   |
| 0.2106        | 5.0   | 3750  | 0.4830          | 0.8683   |
| 0.1439        | 6.0   | 4500  | 0.4916          | 0.8683   |
| 0.0791        | 7.0   | 5250  | 0.5811          | 0.875    |
| 0.077         | 8.0   | 6000  | 0.7477          | 0.8617   |
| 0.0484        | 9.0   | 6750  | 0.9268          | 0.8383   |
| 0.0478        | 10.0  | 7500  | 0.8560          | 0.855    |
| 0.0454        | 11.0  | 8250  | 0.7616          | 0.8733   |
| 0.0414        | 12.0  | 9000  | 0.8591          | 0.8617   |
| 0.0224        | 13.0  | 9750  | 0.8231          | 0.8833   |
| 0.0062        | 14.0  | 10500 | 0.9264          | 0.8717   |
| 0.004         | 15.0  | 11250 | 0.8932          | 0.8783   |
| 0.0299        | 16.0  | 12000 | 0.8030          | 0.8733   |
| 0.0268        | 17.0  | 12750 | 0.8616          | 0.88     |
| 0.0071        | 18.0  | 13500 | 0.9511          | 0.8767   |
| 0.0023        | 19.0  | 14250 | 0.9282          | 0.8783   |
| 0.008         | 20.0  | 15000 | 1.1898          | 0.855    |
| 0.0085        | 21.0  | 15750 | 1.0698          | 0.8683   |
| 0.0003        | 22.0  | 16500 | 1.1571          | 0.8633   |
| 0.0004        | 23.0  | 17250 | 1.1256          | 0.8783   |
| 0.0035        | 24.0  | 18000 | 1.2671          | 0.8633   |
| 0.0           | 25.0  | 18750 | 1.1579          | 0.8683   |
| 0.002         | 26.0  | 19500 | 1.2159          | 0.87     |
| 0.0001        | 27.0  | 20250 | 1.2282          | 0.8717   |
| 0.0           | 28.0  | 21000 | 1.2713          | 0.8683   |
| 0.0           | 29.0  | 21750 | 1.3150          | 0.8683   |
| 0.0           | 30.0  | 22500 | 1.2639          | 0.8733   |
| 0.0           | 31.0  | 23250 | 1.4238          | 0.865    |
| 0.0           | 32.0  | 24000 | 1.3138          | 0.8717   |
| 0.0           | 33.0  | 24750 | 1.4236          | 0.8733   |
| 0.0           | 34.0  | 25500 | 1.4930          | 0.865    |
| 0.0           | 35.0  | 26250 | 1.4369          | 0.87     |
| 0.0           | 36.0  | 27000 | 1.4573          | 0.8667   |
| 0.0           | 37.0  | 27750 | 1.4567          | 0.8717   |
| 0.0           | 38.0  | 28500 | 1.4973          | 0.8767   |
| 0.0           | 39.0  | 29250 | 1.5427          | 0.8667   |
| 0.0           | 40.0  | 30000 | 1.5656          | 0.8717   |
| 0.0           | 41.0  | 30750 | 1.5787          | 0.8717   |
| 0.0           | 42.0  | 31500 | 1.5845          | 0.87     |
| 0.0           | 43.0  | 32250 | 1.5904          | 0.8717   |
| 0.0           | 44.0  | 33000 | 1.5995          | 0.8717   |
| 0.0           | 45.0  | 33750 | 1.6192          | 0.8717   |
| 0.0           | 46.0  | 34500 | 1.6307          | 0.8717   |
| 0.0           | 47.0  | 35250 | 1.6406          | 0.8733   |
| 0.0           | 48.0  | 36000 | 1.6477          | 0.8733   |
| 0.0           | 49.0  | 36750 | 1.6529          | 0.8733   |
| 0.0           | 50.0  | 37500 | 1.6552          | 0.8733   |


### 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