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# smids_3x_deit_small_rms_00001_fold2

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: 1.2809
- Accuracy: 0.8686

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2767        | 1.0   | 225   | 0.3185          | 0.8686   |
| 0.1511        | 2.0   | 450   | 0.3126          | 0.8669   |
| 0.096         | 3.0   | 675   | 0.3591          | 0.8735   |
| 0.0576        | 4.0   | 900   | 0.4235          | 0.8735   |
| 0.0262        | 5.0   | 1125  | 0.4922          | 0.8819   |
| 0.0164        | 6.0   | 1350  | 0.6380          | 0.8719   |
| 0.0169        | 7.0   | 1575  | 0.7607          | 0.8569   |
| 0.0085        | 8.0   | 1800  | 0.8747          | 0.8602   |
| 0.0061        | 9.0   | 2025  | 0.9757          | 0.8586   |
| 0.002         | 10.0  | 2250  | 1.0480          | 0.8586   |
| 0.0017        | 11.0  | 2475  | 0.9765          | 0.8702   |
| 0.0016        | 12.0  | 2700  | 0.9383          | 0.8719   |
| 0.0018        | 13.0  | 2925  | 0.9688          | 0.8719   |
| 0.0           | 14.0  | 3150  | 0.9770          | 0.8602   |
| 0.0002        | 15.0  | 3375  | 0.9981          | 0.8686   |
| 0.0           | 16.0  | 3600  | 0.9902          | 0.8735   |
| 0.0075        | 17.0  | 3825  | 1.0861          | 0.8586   |
| 0.0092        | 18.0  | 4050  | 1.0830          | 0.8552   |
| 0.0002        | 19.0  | 4275  | 0.9892          | 0.8719   |
| 0.0029        | 20.0  | 4500  | 1.1768          | 0.8619   |
| 0.0           | 21.0  | 4725  | 1.1820          | 0.8619   |
| 0.031         | 22.0  | 4950  | 1.0285          | 0.8619   |
| 0.0053        | 23.0  | 5175  | 1.0925          | 0.8569   |
| 0.0           | 24.0  | 5400  | 1.1089          | 0.8652   |
| 0.0412        | 25.0  | 5625  | 1.2047          | 0.8502   |
| 0.0           | 26.0  | 5850  | 1.1861          | 0.8569   |
| 0.0           | 27.0  | 6075  | 1.2680          | 0.8569   |
| 0.0001        | 28.0  | 6300  | 1.1737          | 0.8652   |
| 0.0173        | 29.0  | 6525  | 1.2944          | 0.8486   |
| 0.0044        | 30.0  | 6750  | 1.1884          | 0.8636   |
| 0.0           | 31.0  | 6975  | 1.2534          | 0.8652   |
| 0.0           | 32.0  | 7200  | 1.2427          | 0.8636   |
| 0.0           | 33.0  | 7425  | 1.2253          | 0.8719   |
| 0.0           | 34.0  | 7650  | 1.2543          | 0.8652   |
| 0.0           | 35.0  | 7875  | 1.2431          | 0.8702   |
| 0.004         | 36.0  | 8100  | 1.2651          | 0.8619   |
| 0.0           | 37.0  | 8325  | 1.2443          | 0.8652   |
| 0.0           | 38.0  | 8550  | 1.2852          | 0.8669   |
| 0.004         | 39.0  | 8775  | 1.2690          | 0.8686   |
| 0.0           | 40.0  | 9000  | 1.2725          | 0.8686   |
| 0.0           | 41.0  | 9225  | 1.2668          | 0.8719   |
| 0.0           | 42.0  | 9450  | 1.2758          | 0.8686   |
| 0.0           | 43.0  | 9675  | 1.2725          | 0.8669   |
| 0.0           | 44.0  | 9900  | 1.2814          | 0.8669   |
| 0.0           | 45.0  | 10125 | 1.2808          | 0.8686   |
| 0.0           | 46.0  | 10350 | 1.2792          | 0.8702   |
| 0.0           | 47.0  | 10575 | 1.2803          | 0.8686   |
| 0.0           | 48.0  | 10800 | 1.2804          | 0.8686   |
| 0.0022        | 49.0  | 11025 | 1.2805          | 0.8686   |
| 0.0022        | 50.0  | 11250 | 1.2809          | 0.8686   |


### 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