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# smids_3x_deit_base_adamax_0001_fold2

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: 0.9621
- Accuracy: 0.8819

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.283         | 1.0   | 225   | 0.2854          | 0.8785   |
| 0.1203        | 2.0   | 450   | 0.3573          | 0.8719   |
| 0.0448        | 3.0   | 675   | 0.4385          | 0.8785   |
| 0.0289        | 4.0   | 900   | 0.5496          | 0.8719   |
| 0.0119        | 5.0   | 1125  | 0.6072          | 0.8785   |
| 0.0601        | 6.0   | 1350  | 0.7142          | 0.8752   |
| 0.0063        | 7.0   | 1575  | 0.6692          | 0.8835   |
| 0.0179        | 8.0   | 1800  | 0.6438          | 0.8802   |
| 0.007         | 9.0   | 2025  | 0.7381          | 0.8918   |
| 0.0008        | 10.0  | 2250  | 0.8145          | 0.8835   |
| 0.0007        | 11.0  | 2475  | 0.7915          | 0.8785   |
| 0.0001        | 12.0  | 2700  | 0.8073          | 0.8819   |
| 0.0002        | 13.0  | 2925  | 0.8884          | 0.8819   |
| 0.0           | 14.0  | 3150  | 0.8205          | 0.8902   |
| 0.0           | 15.0  | 3375  | 0.8044          | 0.8785   |
| 0.0           | 16.0  | 3600  | 0.8365          | 0.8752   |
| 0.0029        | 17.0  | 3825  | 0.8450          | 0.8785   |
| 0.0           | 18.0  | 4050  | 0.7920          | 0.8935   |
| 0.0           | 19.0  | 4275  | 0.8363          | 0.8819   |
| 0.0           | 20.0  | 4500  | 0.8889          | 0.8852   |
| 0.0           | 21.0  | 4725  | 0.8448          | 0.8852   |
| 0.0           | 22.0  | 4950  | 0.9311          | 0.8686   |
| 0.0029        | 23.0  | 5175  | 0.9101          | 0.8719   |
| 0.0           | 24.0  | 5400  | 0.8819          | 0.8835   |
| 0.004         | 25.0  | 5625  | 0.8836          | 0.8769   |
| 0.0           | 26.0  | 5850  | 0.8454          | 0.8869   |
| 0.0           | 27.0  | 6075  | 0.8634          | 0.8819   |
| 0.0           | 28.0  | 6300  | 0.8917          | 0.8852   |
| 0.0           | 29.0  | 6525  | 0.9393          | 0.8719   |
| 0.0031        | 30.0  | 6750  | 0.8807          | 0.8885   |
| 0.0           | 31.0  | 6975  | 0.9119          | 0.8719   |
| 0.0           | 32.0  | 7200  | 0.9056          | 0.8785   |
| 0.0           | 33.0  | 7425  | 0.9065          | 0.8785   |
| 0.0           | 34.0  | 7650  | 0.9233          | 0.8785   |
| 0.0           | 35.0  | 7875  | 0.9182          | 0.8835   |
| 0.0027        | 36.0  | 8100  | 0.9235          | 0.8752   |
| 0.0           | 37.0  | 8325  | 0.9102          | 0.8835   |
| 0.0           | 38.0  | 8550  | 0.9395          | 0.8819   |
| 0.0031        | 39.0  | 8775  | 0.9367          | 0.8819   |
| 0.0           | 40.0  | 9000  | 0.9527          | 0.8819   |
| 0.0           | 41.0  | 9225  | 0.9525          | 0.8769   |
| 0.0           | 42.0  | 9450  | 0.9483          | 0.8852   |
| 0.0           | 43.0  | 9675  | 0.9512          | 0.8819   |
| 0.0           | 44.0  | 9900  | 0.9531          | 0.8819   |
| 0.0           | 45.0  | 10125 | 0.9615          | 0.8802   |
| 0.0           | 46.0  | 10350 | 0.9575          | 0.8819   |
| 0.0           | 47.0  | 10575 | 0.9539          | 0.8819   |
| 0.0           | 48.0  | 10800 | 0.9592          | 0.8819   |
| 0.0023        | 49.0  | 11025 | 0.9609          | 0.8819   |
| 0.0023        | 50.0  | 11250 | 0.9621          | 0.8819   |


### 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