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# smids_3x_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: 1.0668
- Accuracy: 0.8752

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2993        | 1.0   | 225   | 0.3110          | 0.8719   |
| 0.214         | 2.0   | 450   | 0.3099          | 0.8719   |
| 0.1117        | 3.0   | 675   | 0.3255          | 0.8835   |
| 0.1732        | 4.0   | 900   | 0.3969          | 0.8636   |
| 0.1001        | 5.0   | 1125  | 0.4203          | 0.8735   |
| 0.0568        | 6.0   | 1350  | 0.5015          | 0.8735   |
| 0.0354        | 7.0   | 1575  | 0.5359          | 0.8769   |
| 0.0128        | 8.0   | 1800  | 0.6487          | 0.8769   |
| 0.012         | 9.0   | 2025  | 0.7872          | 0.8552   |
| 0.0123        | 10.0  | 2250  | 0.8404          | 0.8752   |
| 0.0004        | 11.0  | 2475  | 0.8481          | 0.8652   |
| 0.0319        | 12.0  | 2700  | 0.9253          | 0.8686   |
| 0.0001        | 13.0  | 2925  | 0.9570          | 0.8636   |
| 0.0029        | 14.0  | 3150  | 0.9176          | 0.8702   |
| 0.0009        | 15.0  | 3375  | 1.0326          | 0.8785   |
| 0.0207        | 16.0  | 3600  | 1.0323          | 0.8719   |
| 0.0113        | 17.0  | 3825  | 1.0675          | 0.8686   |
| 0.0006        | 18.0  | 4050  | 1.0013          | 0.8769   |
| 0.0           | 19.0  | 4275  | 1.1724          | 0.8669   |
| 0.0           | 20.0  | 4500  | 0.9929          | 0.8735   |
| 0.0002        | 21.0  | 4725  | 0.9953          | 0.8719   |
| 0.0           | 22.0  | 4950  | 1.1132          | 0.8735   |
| 0.0425        | 23.0  | 5175  | 1.0471          | 0.8686   |
| 0.0           | 24.0  | 5400  | 1.1403          | 0.8652   |
| 0.0141        | 25.0  | 5625  | 1.1287          | 0.8619   |
| 0.0           | 26.0  | 5850  | 0.9874          | 0.8785   |
| 0.0           | 27.0  | 6075  | 1.0105          | 0.8752   |
| 0.0           | 28.0  | 6300  | 1.0130          | 0.8802   |
| 0.0           | 29.0  | 6525  | 1.0721          | 0.8652   |
| 0.0053        | 30.0  | 6750  | 1.0713          | 0.8819   |
| 0.0           | 31.0  | 6975  | 1.0241          | 0.8835   |
| 0.0           | 32.0  | 7200  | 1.0643          | 0.8869   |
| 0.0           | 33.0  | 7425  | 1.0320          | 0.8669   |
| 0.0           | 34.0  | 7650  | 1.0424          | 0.8802   |
| 0.0           | 35.0  | 7875  | 1.0176          | 0.8802   |
| 0.0044        | 36.0  | 8100  | 0.9778          | 0.8785   |
| 0.0           | 37.0  | 8325  | 0.9990          | 0.8819   |
| 0.0           | 38.0  | 8550  | 1.0176          | 0.8752   |
| 0.0047        | 39.0  | 8775  | 1.0819          | 0.8719   |
| 0.0           | 40.0  | 9000  | 1.0393          | 0.8735   |
| 0.0           | 41.0  | 9225  | 1.0424          | 0.8735   |
| 0.0           | 42.0  | 9450  | 1.0459          | 0.8702   |
| 0.0           | 43.0  | 9675  | 1.0528          | 0.8752   |
| 0.0           | 44.0  | 9900  | 1.0545          | 0.8769   |
| 0.0           | 45.0  | 10125 | 1.0566          | 0.8785   |
| 0.0           | 46.0  | 10350 | 1.0564          | 0.8769   |
| 0.0           | 47.0  | 10575 | 1.0599          | 0.8785   |
| 0.0           | 48.0  | 10800 | 1.0618          | 0.8785   |
| 0.002         | 49.0  | 11025 | 1.0652          | 0.8752   |
| 0.002         | 50.0  | 11250 | 1.0668          | 0.8752   |


### 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