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# smids_10x_deit_tiny_adamax_00001_fold4

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: 1.3154
- Accuracy: 0.87

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2882        | 1.0   | 750   | 0.3671          | 0.8467   |
| 0.2123        | 2.0   | 1500  | 0.3694          | 0.8633   |
| 0.189         | 3.0   | 2250  | 0.3920          | 0.8617   |
| 0.1612        | 4.0   | 3000  | 0.4004          | 0.8733   |
| 0.1046        | 5.0   | 3750  | 0.4710          | 0.85     |
| 0.0761        | 6.0   | 4500  | 0.4693          | 0.86     |
| 0.0433        | 7.0   | 5250  | 0.5028          | 0.8633   |
| 0.0491        | 8.0   | 6000  | 0.5468          | 0.865    |
| 0.0675        | 9.0   | 6750  | 0.5733          | 0.8717   |
| 0.039         | 10.0  | 7500  | 0.6491          | 0.875    |
| 0.0538        | 11.0  | 8250  | 0.7887          | 0.8567   |
| 0.012         | 12.0  | 9000  | 0.7915          | 0.87     |
| 0.0116        | 13.0  | 9750  | 0.8692          | 0.87     |
| 0.0189        | 14.0  | 10500 | 0.8744          | 0.8717   |
| 0.0002        | 15.0  | 11250 | 0.9425          | 0.8683   |
| 0.024         | 16.0  | 12000 | 0.9688          | 0.8633   |
| 0.0001        | 17.0  | 12750 | 0.9969          | 0.87     |
| 0.0001        | 18.0  | 13500 | 1.0452          | 0.865    |
| 0.0001        | 19.0  | 14250 | 1.0740          | 0.87     |
| 0.0           | 20.0  | 15000 | 1.0493          | 0.8733   |
| 0.0           | 21.0  | 15750 | 1.0980          | 0.87     |
| 0.0           | 22.0  | 16500 | 1.1024          | 0.8683   |
| 0.0           | 23.0  | 17250 | 1.1005          | 0.8733   |
| 0.0           | 24.0  | 18000 | 1.1084          | 0.875    |
| 0.0           | 25.0  | 18750 | 1.1253          | 0.865    |
| 0.0           | 26.0  | 19500 | 1.1772          | 0.87     |
| 0.0           | 27.0  | 20250 | 1.1989          | 0.87     |
| 0.0           | 28.0  | 21000 | 1.2090          | 0.87     |
| 0.0           | 29.0  | 21750 | 1.2004          | 0.8683   |
| 0.0           | 30.0  | 22500 | 1.2111          | 0.87     |
| 0.0           | 31.0  | 23250 | 1.2258          | 0.87     |
| 0.0           | 32.0  | 24000 | 1.2322          | 0.87     |
| 0.0           | 33.0  | 24750 | 1.2293          | 0.87     |
| 0.0           | 34.0  | 25500 | 1.2340          | 0.8683   |
| 0.0           | 35.0  | 26250 | 1.2422          | 0.8667   |
| 0.0           | 36.0  | 27000 | 1.2597          | 0.8667   |
| 0.0           | 37.0  | 27750 | 1.2593          | 0.8683   |
| 0.0           | 38.0  | 28500 | 1.2651          | 0.87     |
| 0.0           | 39.0  | 29250 | 1.2897          | 0.8667   |
| 0.0           | 40.0  | 30000 | 1.2906          | 0.87     |
| 0.0           | 41.0  | 30750 | 1.2961          | 0.87     |
| 0.0           | 42.0  | 31500 | 1.2976          | 0.8683   |
| 0.0           | 43.0  | 32250 | 1.2961          | 0.87     |
| 0.0           | 44.0  | 33000 | 1.3012          | 0.8683   |
| 0.0           | 45.0  | 33750 | 1.3074          | 0.87     |
| 0.0           | 46.0  | 34500 | 1.3114          | 0.8683   |
| 0.0           | 47.0  | 35250 | 1.3127          | 0.87     |
| 0.0           | 48.0  | 36000 | 1.3140          | 0.87     |
| 0.0           | 49.0  | 36750 | 1.3150          | 0.87     |
| 0.0           | 50.0  | 37500 | 1.3154          | 0.87     |


### 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