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# smids_10x_deit_small_rms_0001_fold5

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: 1.1181
- Accuracy: 0.8983

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1916        | 1.0   | 750   | 0.2932          | 0.8983   |
| 0.1713        | 2.0   | 1500  | 0.3029          | 0.9083   |
| 0.1109        | 3.0   | 2250  | 0.5067          | 0.8767   |
| 0.0732        | 4.0   | 3000  | 0.4780          | 0.91     |
| 0.102         | 5.0   | 3750  | 0.4476          | 0.8983   |
| 0.0951        | 6.0   | 4500  | 0.5447          | 0.9017   |
| 0.0348        | 7.0   | 5250  | 0.5626          | 0.915    |
| 0.0144        | 8.0   | 6000  | 0.5893          | 0.9133   |
| 0.0592        | 9.0   | 6750  | 0.5568          | 0.91     |
| 0.0154        | 10.0  | 7500  | 0.5698          | 0.9017   |
| 0.0271        | 11.0  | 8250  | 0.6256          | 0.8983   |
| 0.0176        | 12.0  | 9000  | 0.7391          | 0.8917   |
| 0.012         | 13.0  | 9750  | 0.6256          | 0.8967   |
| 0.0094        | 14.0  | 10500 | 0.7473          | 0.895    |
| 0.0301        | 15.0  | 11250 | 0.6066          | 0.905    |
| 0.0069        | 16.0  | 12000 | 0.6970          | 0.9      |
| 0.0131        | 17.0  | 12750 | 0.6902          | 0.895    |
| 0.0022        | 18.0  | 13500 | 0.7962          | 0.8833   |
| 0.0162        | 19.0  | 14250 | 0.8033          | 0.8967   |
| 0.0045        | 20.0  | 15000 | 0.7612          | 0.8933   |
| 0.0063        | 21.0  | 15750 | 0.7939          | 0.9      |
| 0.0002        | 22.0  | 16500 | 0.7612          | 0.8933   |
| 0.0161        | 23.0  | 17250 | 0.8161          | 0.8867   |
| 0.0001        | 24.0  | 18000 | 0.8196          | 0.8933   |
| 0.038         | 25.0  | 18750 | 0.8702          | 0.8883   |
| 0.0257        | 26.0  | 19500 | 0.7862          | 0.8983   |
| 0.0004        | 27.0  | 20250 | 0.8138          | 0.8983   |
| 0.0001        | 28.0  | 21000 | 0.8830          | 0.905    |
| 0.0003        | 29.0  | 21750 | 1.0169          | 0.89     |
| 0.016         | 30.0  | 22500 | 0.8531          | 0.8883   |
| 0.0027        | 31.0  | 23250 | 0.9699          | 0.895    |
| 0.0379        | 32.0  | 24000 | 1.0313          | 0.89     |
| 0.0307        | 33.0  | 24750 | 0.8698          | 0.905    |
| 0.0           | 34.0  | 25500 | 0.8949          | 0.9017   |
| 0.0           | 35.0  | 26250 | 0.9260          | 0.8917   |
| 0.0           | 36.0  | 27000 | 0.9677          | 0.89     |
| 0.0036        | 37.0  | 27750 | 1.0175          | 0.9017   |
| 0.0           | 38.0  | 28500 | 1.0579          | 0.8967   |
| 0.0003        | 39.0  | 29250 | 1.1044          | 0.8917   |
| 0.0           | 40.0  | 30000 | 1.0357          | 0.895    |
| 0.0           | 41.0  | 30750 | 1.0321          | 0.9      |
| 0.0           | 42.0  | 31500 | 1.0802          | 0.895    |
| 0.0           | 43.0  | 32250 | 1.1064          | 0.895    |
| 0.0           | 44.0  | 33000 | 1.1154          | 0.9017   |
| 0.0           | 45.0  | 33750 | 1.1069          | 0.9017   |
| 0.0           | 46.0  | 34500 | 1.1200          | 0.895    |
| 0.0           | 47.0  | 35250 | 1.1205          | 0.8967   |
| 0.0           | 48.0  | 36000 | 1.1169          | 0.8983   |
| 0.0           | 49.0  | 36750 | 1.1164          | 0.8983   |
| 0.0           | 50.0  | 37500 | 1.1181          | 0.8983   |


### 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