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# smids_3x_deit_base_adamax_001_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: 1.0193
- Accuracy: 0.8867

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3633        | 1.0   | 225   | 0.3986          | 0.8483   |
| 0.2906        | 2.0   | 450   | 0.3096          | 0.8867   |
| 0.2237        | 3.0   | 675   | 0.4059          | 0.8517   |
| 0.2184        | 4.0   | 900   | 0.3380          | 0.8917   |
| 0.1738        | 5.0   | 1125  | 0.3923          | 0.8833   |
| 0.0994        | 6.0   | 1350  | 0.4783          | 0.8667   |
| 0.1643        | 7.0   | 1575  | 0.3825          | 0.8983   |
| 0.1204        | 8.0   | 1800  | 0.4481          | 0.8683   |
| 0.0708        | 9.0   | 2025  | 0.4702          | 0.8883   |
| 0.0392        | 10.0  | 2250  | 0.5947          | 0.9017   |
| 0.0581        | 11.0  | 2475  | 0.5317          | 0.89     |
| 0.102         | 12.0  | 2700  | 0.6171          | 0.8683   |
| 0.0149        | 13.0  | 2925  | 0.4983          | 0.9      |
| 0.0251        | 14.0  | 3150  | 0.5396          | 0.8983   |
| 0.0063        | 15.0  | 3375  | 0.6932          | 0.8833   |
| 0.0424        | 16.0  | 3600  | 0.8036          | 0.865    |
| 0.0007        | 17.0  | 3825  | 0.7423          | 0.8817   |
| 0.022         | 18.0  | 4050  | 0.6506          | 0.8817   |
| 0.0402        | 19.0  | 4275  | 0.6999          | 0.89     |
| 0.0268        | 20.0  | 4500  | 0.8126          | 0.8883   |
| 0.0           | 21.0  | 4725  | 0.8105          | 0.9      |
| 0.0173        | 22.0  | 4950  | 0.8126          | 0.885    |
| 0.0167        | 23.0  | 5175  | 0.7462          | 0.8833   |
| 0.0056        | 24.0  | 5400  | 0.7445          | 0.8917   |
| 0.0002        | 25.0  | 5625  | 0.8258          | 0.8967   |
| 0.0018        | 26.0  | 5850  | 0.7747          | 0.8833   |
| 0.0003        | 27.0  | 6075  | 0.8895          | 0.89     |
| 0.0019        | 28.0  | 6300  | 0.8581          | 0.89     |
| 0.0039        | 29.0  | 6525  | 0.8693          | 0.8917   |
| 0.0           | 30.0  | 6750  | 0.9655          | 0.8867   |
| 0.0           | 31.0  | 6975  | 0.8077          | 0.8867   |
| 0.0           | 32.0  | 7200  | 0.8704          | 0.88     |
| 0.003         | 33.0  | 7425  | 0.8926          | 0.8933   |
| 0.0           | 34.0  | 7650  | 0.9137          | 0.8917   |
| 0.0029        | 35.0  | 7875  | 0.9309          | 0.89     |
| 0.0           | 36.0  | 8100  | 0.9596          | 0.8817   |
| 0.0036        | 37.0  | 8325  | 0.9111          | 0.8817   |
| 0.0009        | 38.0  | 8550  | 0.9317          | 0.8817   |
| 0.0           | 39.0  | 8775  | 0.9642          | 0.88     |
| 0.0           | 40.0  | 9000  | 0.9829          | 0.8817   |
| 0.0           | 41.0  | 9225  | 0.9951          | 0.8817   |
| 0.0           | 42.0  | 9450  | 1.0003          | 0.8817   |
| 0.0029        | 43.0  | 9675  | 0.9978          | 0.8833   |
| 0.0           | 44.0  | 9900  | 0.9820          | 0.8867   |
| 0.0           | 45.0  | 10125 | 0.9878          | 0.89     |
| 0.0           | 46.0  | 10350 | 1.0055          | 0.8883   |
| 0.0           | 47.0  | 10575 | 1.0104          | 0.8867   |
| 0.0           | 48.0  | 10800 | 1.0144          | 0.8867   |
| 0.0           | 49.0  | 11025 | 1.0175          | 0.8867   |
| 0.0           | 50.0  | 11250 | 1.0193          | 0.8867   |


### 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