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# smids_3x_deit_small_adamax_001_fold3

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9786
- Accuracy: 0.8933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4746        | 1.0   | 225   | 0.4469          | 0.7917   |
| 0.3712        | 2.0   | 450   | 0.4363          | 0.8183   |
| 0.2858        | 3.0   | 675   | 0.3457          | 0.8733   |
| 0.1805        | 4.0   | 900   | 0.4133          | 0.865    |
| 0.1699        | 5.0   | 1125  | 0.3336          | 0.89     |
| 0.1967        | 6.0   | 1350  | 0.3958          | 0.88     |
| 0.1372        | 7.0   | 1575  | 0.4702          | 0.87     |
| 0.1698        | 8.0   | 1800  | 0.3428          | 0.8867   |
| 0.1529        | 9.0   | 2025  | 0.5351          | 0.8583   |
| 0.0621        | 10.0  | 2250  | 0.5492          | 0.8733   |
| 0.1311        | 11.0  | 2475  | 0.5821          | 0.8767   |
| 0.1035        | 12.0  | 2700  | 0.6050          | 0.8667   |
| 0.0328        | 13.0  | 2925  | 0.6304          | 0.875    |
| 0.036         | 14.0  | 3150  | 0.5969          | 0.8983   |
| 0.0422        | 15.0  | 3375  | 0.6326          | 0.8817   |
| 0.0088        | 16.0  | 3600  | 0.6452          | 0.8733   |
| 0.0148        | 17.0  | 3825  | 0.5210          | 0.8883   |
| 0.0267        | 18.0  | 4050  | 0.6134          | 0.88     |
| 0.0523        | 19.0  | 4275  | 0.6448          | 0.8933   |
| 0.0182        | 20.0  | 4500  | 0.7298          | 0.8783   |
| 0.0004        | 21.0  | 4725  | 0.7641          | 0.8683   |
| 0.0037        | 22.0  | 4950  | 0.8419          | 0.87     |
| 0.0038        | 23.0  | 5175  | 0.7762          | 0.8817   |
| 0.0002        | 24.0  | 5400  | 0.7488          | 0.8883   |
| 0.0367        | 25.0  | 5625  | 0.6912          | 0.8767   |
| 0.0002        | 26.0  | 5850  | 0.6890          | 0.895    |
| 0.0002        | 27.0  | 6075  | 0.6160          | 0.9017   |
| 0.0001        | 28.0  | 6300  | 0.6922          | 0.8967   |
| 0.007         | 29.0  | 6525  | 0.8317          | 0.885    |
| 0.0083        | 30.0  | 6750  | 0.6909          | 0.8983   |
| 0.0048        | 31.0  | 6975  | 0.7613          | 0.8967   |
| 0.0           | 32.0  | 7200  | 0.8055          | 0.895    |
| 0.0           | 33.0  | 7425  | 0.8267          | 0.8917   |
| 0.0           | 34.0  | 7650  | 0.8303          | 0.8917   |
| 0.0           | 35.0  | 7875  | 0.8741          | 0.8967   |
| 0.0           | 36.0  | 8100  | 0.8381          | 0.8967   |
| 0.0024        | 37.0  | 8325  | 0.8583          | 0.8983   |
| 0.0           | 38.0  | 8550  | 0.9234          | 0.8917   |
| 0.0           | 39.0  | 8775  | 0.8565          | 0.8967   |
| 0.0           | 40.0  | 9000  | 0.8898          | 0.8917   |
| 0.0           | 41.0  | 9225  | 0.9312          | 0.8917   |
| 0.0           | 42.0  | 9450  | 0.9410          | 0.8933   |
| 0.0           | 43.0  | 9675  | 0.9514          | 0.8933   |
| 0.0           | 44.0  | 9900  | 0.9553          | 0.8933   |
| 0.0           | 45.0  | 10125 | 0.9599          | 0.8933   |
| 0.0           | 46.0  | 10350 | 0.9610          | 0.8933   |
| 0.0027        | 47.0  | 10575 | 0.9689          | 0.8933   |
| 0.0           | 48.0  | 10800 | 0.9724          | 0.8933   |
| 0.0           | 49.0  | 11025 | 0.9761          | 0.8933   |
| 0.0           | 50.0  | 11250 | 0.9786          | 0.8933   |


### 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