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# smids_10x_deit_tiny_adamax_0001_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: 1.1802
- Accuracy: 0.8917

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2287        | 1.0   | 750   | 0.3453          | 0.8783   |
| 0.1774        | 2.0   | 1500  | 0.3819          | 0.8783   |
| 0.1288        | 3.0   | 2250  | 0.5077          | 0.8617   |
| 0.0591        | 4.0   | 3000  | 0.5258          | 0.8767   |
| 0.013         | 5.0   | 3750  | 0.7962          | 0.87     |
| 0.0441        | 6.0   | 4500  | 0.7798          | 0.875    |
| 0.0022        | 7.0   | 5250  | 0.8865          | 0.8783   |
| 0.0168        | 8.0   | 6000  | 0.9982          | 0.8817   |
| 0.0002        | 9.0   | 6750  | 0.9825          | 0.8833   |
| 0.012         | 10.0  | 7500  | 0.9837          | 0.8883   |
| 0.0139        | 11.0  | 8250  | 1.0185          | 0.88     |
| 0.0283        | 12.0  | 9000  | 1.0469          | 0.8767   |
| 0.0013        | 13.0  | 9750  | 1.1375          | 0.885    |
| 0.0051        | 14.0  | 10500 | 1.1468          | 0.8817   |
| 0.0           | 15.0  | 11250 | 1.1486          | 0.875    |
| 0.0211        | 16.0  | 12000 | 1.0421          | 0.8867   |
| 0.0           | 17.0  | 12750 | 1.1215          | 0.8783   |
| 0.0           | 18.0  | 13500 | 1.1501          | 0.8917   |
| 0.0001        | 19.0  | 14250 | 1.2352          | 0.88     |
| 0.0002        | 20.0  | 15000 | 1.2860          | 0.8883   |
| 0.0           | 21.0  | 15750 | 1.1704          | 0.8833   |
| 0.0           | 22.0  | 16500 | 1.0833          | 0.8933   |
| 0.0           | 23.0  | 17250 | 1.1109          | 0.8933   |
| 0.0           | 24.0  | 18000 | 1.1424          | 0.8933   |
| 0.0           | 25.0  | 18750 | 1.0812          | 0.89     |
| 0.0           | 26.0  | 19500 | 1.1046          | 0.8917   |
| 0.0           | 27.0  | 20250 | 1.1453          | 0.8883   |
| 0.0           | 28.0  | 21000 | 1.1203          | 0.885    |
| 0.0           | 29.0  | 21750 | 1.1015          | 0.8933   |
| 0.0           | 30.0  | 22500 | 1.1212          | 0.8967   |
| 0.0           | 31.0  | 23250 | 1.1480          | 0.8883   |
| 0.0           | 32.0  | 24000 | 1.1454          | 0.8833   |
| 0.0           | 33.0  | 24750 | 1.1314          | 0.8867   |
| 0.0           | 34.0  | 25500 | 1.1208          | 0.885    |
| 0.0           | 35.0  | 26250 | 1.1448          | 0.8833   |
| 0.0           | 36.0  | 27000 | 1.1486          | 0.8833   |
| 0.0           | 37.0  | 27750 | 1.1572          | 0.885    |
| 0.0           | 38.0  | 28500 | 1.1406          | 0.8867   |
| 0.0           | 39.0  | 29250 | 1.1768          | 0.89     |
| 0.0           | 40.0  | 30000 | 1.1690          | 0.885    |
| 0.0           | 41.0  | 30750 | 1.1715          | 0.8883   |
| 0.0           | 42.0  | 31500 | 1.1720          | 0.89     |
| 0.0           | 43.0  | 32250 | 1.1654          | 0.8917   |
| 0.0           | 44.0  | 33000 | 1.1692          | 0.8917   |
| 0.0           | 45.0  | 33750 | 1.1750          | 0.8917   |
| 0.0           | 46.0  | 34500 | 1.1770          | 0.8917   |
| 0.0           | 47.0  | 35250 | 1.1783          | 0.8917   |
| 0.0           | 48.0  | 36000 | 1.1786          | 0.8917   |
| 0.0           | 49.0  | 36750 | 1.1796          | 0.8917   |
| 0.0           | 50.0  | 37500 | 1.1802          | 0.8917   |


### 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