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# smids_3x_deit_small_sgd_001_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: 0.3562
- Accuracy: 0.8569

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8599        | 1.0   | 225   | 0.8308          | 0.6789   |
| 0.6572        | 2.0   | 450   | 0.6508          | 0.7488   |
| 0.5628        | 3.0   | 675   | 0.5574          | 0.7920   |
| 0.4888        | 4.0   | 900   | 0.5053          | 0.8053   |
| 0.4209        | 5.0   | 1125  | 0.4713          | 0.8136   |
| 0.3729        | 6.0   | 1350  | 0.4472          | 0.8253   |
| 0.4088        | 7.0   | 1575  | 0.4292          | 0.8270   |
| 0.3871        | 8.0   | 1800  | 0.4168          | 0.8353   |
| 0.3605        | 9.0   | 2025  | 0.4049          | 0.8369   |
| 0.2963        | 10.0  | 2250  | 0.3978          | 0.8403   |
| 0.3519        | 11.0  | 2475  | 0.3893          | 0.8469   |
| 0.3126        | 12.0  | 2700  | 0.3814          | 0.8536   |
| 0.2889        | 13.0  | 2925  | 0.3781          | 0.8519   |
| 0.3096        | 14.0  | 3150  | 0.3739          | 0.8552   |
| 0.3153        | 15.0  | 3375  | 0.3694          | 0.8586   |
| 0.3271        | 16.0  | 3600  | 0.3680          | 0.8619   |
| 0.2697        | 17.0  | 3825  | 0.3655          | 0.8602   |
| 0.2138        | 18.0  | 4050  | 0.3624          | 0.8602   |
| 0.2422        | 19.0  | 4275  | 0.3602          | 0.8636   |
| 0.288         | 20.0  | 4500  | 0.3609          | 0.8652   |
| 0.3039        | 21.0  | 4725  | 0.3587          | 0.8619   |
| 0.2907        | 22.0  | 4950  | 0.3580          | 0.8619   |
| 0.3138        | 23.0  | 5175  | 0.3576          | 0.8652   |
| 0.2718        | 24.0  | 5400  | 0.3569          | 0.8669   |
| 0.263         | 25.0  | 5625  | 0.3551          | 0.8669   |
| 0.245         | 26.0  | 5850  | 0.3538          | 0.8686   |
| 0.2019        | 27.0  | 6075  | 0.3530          | 0.8636   |
| 0.2353        | 28.0  | 6300  | 0.3539          | 0.8636   |
| 0.2451        | 29.0  | 6525  | 0.3558          | 0.8602   |
| 0.2565        | 30.0  | 6750  | 0.3536          | 0.8652   |
| 0.2202        | 31.0  | 6975  | 0.3542          | 0.8636   |
| 0.2433        | 32.0  | 7200  | 0.3552          | 0.8636   |
| 0.2621        | 33.0  | 7425  | 0.3534          | 0.8652   |
| 0.2353        | 34.0  | 7650  | 0.3541          | 0.8652   |
| 0.1836        | 35.0  | 7875  | 0.3533          | 0.8602   |
| 0.2199        | 36.0  | 8100  | 0.3554          | 0.8619   |
| 0.2271        | 37.0  | 8325  | 0.3536          | 0.8602   |
| 0.1937        | 38.0  | 8550  | 0.3541          | 0.8619   |
| 0.1782        | 39.0  | 8775  | 0.3547          | 0.8586   |
| 0.1988        | 40.0  | 9000  | 0.3551          | 0.8586   |
| 0.1613        | 41.0  | 9225  | 0.3546          | 0.8602   |
| 0.1997        | 42.0  | 9450  | 0.3550          | 0.8586   |
| 0.1938        | 43.0  | 9675  | 0.3554          | 0.8569   |
| 0.1972        | 44.0  | 9900  | 0.3557          | 0.8586   |
| 0.2519        | 45.0  | 10125 | 0.3555          | 0.8569   |
| 0.2003        | 46.0  | 10350 | 0.3557          | 0.8569   |
| 0.1846        | 47.0  | 10575 | 0.3560          | 0.8586   |
| 0.1808        | 48.0  | 10800 | 0.3561          | 0.8569   |
| 0.2273        | 49.0  | 11025 | 0.3561          | 0.8569   |
| 0.1583        | 50.0  | 11250 | 0.3562          | 0.8569   |


### 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