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# smids_3x_deit_base_rms_00001_fold5

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9726
- Accuracy: 0.885

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2653        | 1.0   | 225   | 0.2902          | 0.8817   |
| 0.1207        | 2.0   | 450   | 0.2612          | 0.9033   |
| 0.0968        | 3.0   | 675   | 0.3296          | 0.89     |
| 0.0298        | 4.0   | 900   | 0.4559          | 0.8883   |
| 0.0117        | 5.0   | 1125  | 0.4992          | 0.875    |
| 0.0194        | 6.0   | 1350  | 0.4918          | 0.8933   |
| 0.0374        | 7.0   | 1575  | 0.6989          | 0.8833   |
| 0.0219        | 8.0   | 1800  | 0.6248          | 0.8833   |
| 0.0334        | 9.0   | 2025  | 0.5552          | 0.89     |
| 0.0279        | 10.0  | 2250  | 0.6938          | 0.8767   |
| 0.0313        | 11.0  | 2475  | 0.7037          | 0.8933   |
| 0.0467        | 12.0  | 2700  | 0.7727          | 0.8817   |
| 0.0002        | 13.0  | 2925  | 0.7310          | 0.8883   |
| 0.0003        | 14.0  | 3150  | 0.7907          | 0.8883   |
| 0.0042        | 15.0  | 3375  | 0.6719          | 0.8917   |
| 0.0001        | 16.0  | 3600  | 0.7166          | 0.8883   |
| 0.0           | 17.0  | 3825  | 0.7878          | 0.8817   |
| 0.0001        | 18.0  | 4050  | 0.8205          | 0.885    |
| 0.0463        | 19.0  | 4275  | 0.7870          | 0.9017   |
| 0.0071        | 20.0  | 4500  | 0.8873          | 0.8867   |
| 0.0           | 21.0  | 4725  | 0.8762          | 0.89     |
| 0.0           | 22.0  | 4950  | 0.8528          | 0.89     |
| 0.0           | 23.0  | 5175  | 0.8386          | 0.8867   |
| 0.0046        | 24.0  | 5400  | 0.8618          | 0.8867   |
| 0.0           | 25.0  | 5625  | 0.8575          | 0.8883   |
| 0.0           | 26.0  | 5850  | 0.8716          | 0.8883   |
| 0.0           | 27.0  | 6075  | 0.9079          | 0.8833   |
| 0.0           | 28.0  | 6300  | 0.8533          | 0.895    |
| 0.0036        | 29.0  | 6525  | 0.9107          | 0.8883   |
| 0.0           | 30.0  | 6750  | 0.9085          | 0.8917   |
| 0.0           | 31.0  | 6975  | 0.9134          | 0.8883   |
| 0.0           | 32.0  | 7200  | 0.9080          | 0.8933   |
| 0.0034        | 33.0  | 7425  | 0.9660          | 0.8883   |
| 0.0           | 34.0  | 7650  | 0.9464          | 0.8883   |
| 0.0031        | 35.0  | 7875  | 0.9525          | 0.89     |
| 0.0           | 36.0  | 8100  | 0.9223          | 0.8917   |
| 0.0032        | 37.0  | 8325  | 0.9566          | 0.89     |
| 0.0           | 38.0  | 8550  | 0.9486          | 0.8933   |
| 0.0           | 39.0  | 8775  | 0.9542          | 0.8917   |
| 0.0           | 40.0  | 9000  | 0.9546          | 0.8883   |
| 0.0           | 41.0  | 9225  | 0.9671          | 0.8867   |
| 0.0           | 42.0  | 9450  | 0.9604          | 0.885    |
| 0.0026        | 43.0  | 9675  | 0.9661          | 0.8867   |
| 0.0           | 44.0  | 9900  | 0.9673          | 0.8883   |
| 0.0           | 45.0  | 10125 | 0.9685          | 0.8883   |
| 0.0           | 46.0  | 10350 | 0.9692          | 0.8867   |
| 0.0           | 47.0  | 10575 | 0.9716          | 0.8867   |
| 0.0           | 48.0  | 10800 | 0.9722          | 0.8867   |
| 0.0           | 49.0  | 11025 | 0.9726          | 0.8867   |
| 0.0           | 50.0  | 11250 | 0.9726          | 0.885    |


### 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