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# smids_10x_deit_tiny_rms_00001_fold2

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: 1.2287
- Accuracy: 0.8869

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2512        | 1.0   | 750   | 0.3089          | 0.8735   |
| 0.118         | 2.0   | 1500  | 0.3336          | 0.8819   |
| 0.0986        | 3.0   | 2250  | 0.3751          | 0.8735   |
| 0.1049        | 4.0   | 3000  | 0.4900          | 0.8819   |
| 0.0487        | 5.0   | 3750  | 0.5969          | 0.8835   |
| 0.0234        | 6.0   | 4500  | 0.6491          | 0.8835   |
| 0.036         | 7.0   | 5250  | 0.7457          | 0.8869   |
| 0.001         | 8.0   | 6000  | 0.8757          | 0.8719   |
| 0.0148        | 9.0   | 6750  | 0.8581          | 0.8869   |
| 0.0298        | 10.0  | 7500  | 1.0727          | 0.8719   |
| 0.0019        | 11.0  | 8250  | 1.0234          | 0.8719   |
| 0.0146        | 12.0  | 9000  | 1.1199          | 0.8702   |
| 0.0069        | 13.0  | 9750  | 1.0417          | 0.8785   |
| 0.0001        | 14.0  | 10500 | 1.0745          | 0.8819   |
| 0.0           | 15.0  | 11250 | 1.0294          | 0.8802   |
| 0.0091        | 16.0  | 12000 | 1.1025          | 0.8802   |
| 0.0186        | 17.0  | 12750 | 1.0736          | 0.8835   |
| 0.0           | 18.0  | 13500 | 1.0297          | 0.8769   |
| 0.047         | 19.0  | 14250 | 1.1126          | 0.8819   |
| 0.0           | 20.0  | 15000 | 1.1842          | 0.8785   |
| 0.0           | 21.0  | 15750 | 1.2771          | 0.8686   |
| 0.0198        | 22.0  | 16500 | 1.1311          | 0.8869   |
| 0.0357        | 23.0  | 17250 | 1.1425          | 0.8869   |
| 0.0           | 24.0  | 18000 | 1.1413          | 0.8885   |
| 0.0           | 25.0  | 18750 | 1.1558          | 0.8852   |
| 0.0           | 26.0  | 19500 | 1.1246          | 0.8869   |
| 0.0271        | 27.0  | 20250 | 1.2507          | 0.8752   |
| 0.0           | 28.0  | 21000 | 1.1107          | 0.8902   |
| 0.0           | 29.0  | 21750 | 1.1979          | 0.8852   |
| 0.0           | 30.0  | 22500 | 1.2404          | 0.8869   |
| 0.0           | 31.0  | 23250 | 1.2332          | 0.8819   |
| 0.0           | 32.0  | 24000 | 1.3008          | 0.8819   |
| 0.0           | 33.0  | 24750 | 1.3101          | 0.8819   |
| 0.0           | 34.0  | 25500 | 1.3030          | 0.8869   |
| 0.0           | 35.0  | 26250 | 1.1931          | 0.8835   |
| 0.0           | 36.0  | 27000 | 1.2127          | 0.8802   |
| 0.0203        | 37.0  | 27750 | 1.1903          | 0.8802   |
| 0.0           | 38.0  | 28500 | 1.2617          | 0.8869   |
| 0.0           | 39.0  | 29250 | 1.1890          | 0.8935   |
| 0.0           | 40.0  | 30000 | 1.2276          | 0.8869   |
| 0.0           | 41.0  | 30750 | 1.1665          | 0.8885   |
| 0.0           | 42.0  | 31500 | 1.1610          | 0.8852   |
| 0.0           | 43.0  | 32250 | 1.2105          | 0.8902   |
| 0.0           | 44.0  | 33000 | 1.2243          | 0.8885   |
| 0.0031        | 45.0  | 33750 | 1.2267          | 0.8902   |
| 0.0           | 46.0  | 34500 | 1.2227          | 0.8885   |
| 0.0           | 47.0  | 35250 | 1.2254          | 0.8869   |
| 0.0           | 48.0  | 36000 | 1.2244          | 0.8852   |
| 0.0           | 49.0  | 36750 | 1.2292          | 0.8869   |
| 0.0           | 50.0  | 37500 | 1.2287          | 0.8869   |


### 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