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# smids_3x_deit_tiny_rms_0001_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: 1.0507
- Accuracy: 0.8885

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3654        | 1.0   | 225   | 0.3820          | 0.8419   |
| 0.2564        | 2.0   | 450   | 0.3888          | 0.8502   |
| 0.2078        | 3.0   | 675   | 0.3453          | 0.8669   |
| 0.1779        | 4.0   | 900   | 0.3342          | 0.8785   |
| 0.0923        | 5.0   | 1125  | 0.4468          | 0.8702   |
| 0.1133        | 6.0   | 1350  | 0.4712          | 0.8885   |
| 0.1075        | 7.0   | 1575  | 0.5119          | 0.8785   |
| 0.0546        | 8.0   | 1800  | 0.5949          | 0.8852   |
| 0.107         | 9.0   | 2025  | 0.6816          | 0.8619   |
| 0.0417        | 10.0  | 2250  | 0.6436          | 0.8918   |
| 0.0434        | 11.0  | 2475  | 0.6287          | 0.8918   |
| 0.0459        | 12.0  | 2700  | 0.7263          | 0.8802   |
| 0.01          | 13.0  | 2925  | 1.0463          | 0.8586   |
| 0.0718        | 14.0  | 3150  | 0.7632          | 0.8686   |
| 0.0139        | 15.0  | 3375  | 0.8074          | 0.8752   |
| 0.0175        | 16.0  | 3600  | 0.9064          | 0.8819   |
| 0.0398        | 17.0  | 3825  | 0.8900          | 0.8569   |
| 0.0628        | 18.0  | 4050  | 0.8666          | 0.8769   |
| 0.0021        | 19.0  | 4275  | 1.0191          | 0.8636   |
| 0.0486        | 20.0  | 4500  | 0.9743          | 0.8619   |
| 0.0363        | 21.0  | 4725  | 0.8658          | 0.8636   |
| 0.0187        | 22.0  | 4950  | 0.8042          | 0.8802   |
| 0.061         | 23.0  | 5175  | 0.9235          | 0.8735   |
| 0.0107        | 24.0  | 5400  | 0.9113          | 0.8752   |
| 0.0112        | 25.0  | 5625  | 1.0185          | 0.8785   |
| 0.0001        | 26.0  | 5850  | 0.9687          | 0.8636   |
| 0.0001        | 27.0  | 6075  | 0.8990          | 0.8686   |
| 0.0           | 28.0  | 6300  | 0.8022          | 0.8735   |
| 0.0001        | 29.0  | 6525  | 0.9932          | 0.8752   |
| 0.0056        | 30.0  | 6750  | 0.9438          | 0.8785   |
| 0.0025        | 31.0  | 6975  | 0.8626          | 0.8719   |
| 0.0001        | 32.0  | 7200  | 0.8253          | 0.8835   |
| 0.0037        | 33.0  | 7425  | 0.8896          | 0.8952   |
| 0.0001        | 34.0  | 7650  | 0.8932          | 0.8785   |
| 0.0037        | 35.0  | 7875  | 0.9625          | 0.8885   |
| 0.0037        | 36.0  | 8100  | 0.9054          | 0.8869   |
| 0.0           | 37.0  | 8325  | 0.9088          | 0.8802   |
| 0.0           | 38.0  | 8550  | 1.0141          | 0.8719   |
| 0.0067        | 39.0  | 8775  | 1.0333          | 0.8902   |
| 0.0           | 40.0  | 9000  | 0.9904          | 0.8802   |
| 0.0007        | 41.0  | 9225  | 1.0454          | 0.8802   |
| 0.0           | 42.0  | 9450  | 1.0162          | 0.8835   |
| 0.0           | 43.0  | 9675  | 1.0365          | 0.8819   |
| 0.0           | 44.0  | 9900  | 1.0455          | 0.8819   |
| 0.0           | 45.0  | 10125 | 1.0251          | 0.8852   |
| 0.0           | 46.0  | 10350 | 1.0400          | 0.8902   |
| 0.0           | 47.0  | 10575 | 1.0402          | 0.8869   |
| 0.0           | 48.0  | 10800 | 1.0455          | 0.8852   |
| 0.0025        | 49.0  | 11025 | 1.0501          | 0.8885   |
| 0.0025        | 50.0  | 11250 | 1.0507          | 0.8885   |


### 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