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# smids_3x_deit_base_sgd_0001_fold4

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: 0.6280
- Accuracy: 0.7917

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1036        | 1.0   | 225   | 1.0989          | 0.3817   |
| 1.0669        | 2.0   | 450   | 1.0855          | 0.4283   |
| 1.054         | 3.0   | 675   | 1.0721          | 0.4483   |
| 1.0504        | 4.0   | 900   | 1.0584          | 0.4933   |
| 1.0338        | 5.0   | 1125  | 1.0440          | 0.5167   |
| 1.0048        | 6.0   | 1350  | 1.0283          | 0.535    |
| 1.0001        | 7.0   | 1575  | 1.0115          | 0.57     |
| 0.9895        | 8.0   | 1800  | 0.9932          | 0.59     |
| 0.9628        | 9.0   | 2025  | 0.9742          | 0.6033   |
| 0.944         | 10.0  | 2250  | 0.9544          | 0.6283   |
| 0.9313        | 11.0  | 2475  | 0.9345          | 0.63     |
| 0.9444        | 12.0  | 2700  | 0.9144          | 0.6383   |
| 0.8913        | 13.0  | 2925  | 0.8947          | 0.6667   |
| 0.8686        | 14.0  | 3150  | 0.8758          | 0.6783   |
| 0.8767        | 15.0  | 3375  | 0.8579          | 0.7      |
| 0.8703        | 16.0  | 3600  | 0.8412          | 0.7083   |
| 0.8299        | 17.0  | 3825  | 0.8255          | 0.7217   |
| 0.8366        | 18.0  | 4050  | 0.8106          | 0.7267   |
| 0.8333        | 19.0  | 4275  | 0.7965          | 0.735    |
| 0.8046        | 20.0  | 4500  | 0.7834          | 0.7417   |
| 0.7972        | 21.0  | 4725  | 0.7709          | 0.7467   |
| 0.7678        | 22.0  | 4950  | 0.7592          | 0.7533   |
| 0.7921        | 23.0  | 5175  | 0.7481          | 0.755    |
| 0.7726        | 24.0  | 5400  | 0.7377          | 0.76     |
| 0.7352        | 25.0  | 5625  | 0.7280          | 0.7633   |
| 0.7455        | 26.0  | 5850  | 0.7189          | 0.7683   |
| 0.7316        | 27.0  | 6075  | 0.7102          | 0.7733   |
| 0.6935        | 28.0  | 6300  | 0.7023          | 0.7733   |
| 0.7272        | 29.0  | 6525  | 0.6948          | 0.7733   |
| 0.6961        | 30.0  | 6750  | 0.6878          | 0.7783   |
| 0.6838        | 31.0  | 6975  | 0.6813          | 0.7817   |
| 0.684         | 32.0  | 7200  | 0.6752          | 0.7817   |
| 0.6981        | 33.0  | 7425  | 0.6696          | 0.785    |
| 0.6903        | 34.0  | 7650  | 0.6644          | 0.7867   |
| 0.6587        | 35.0  | 7875  | 0.6596          | 0.7883   |
| 0.6872        | 36.0  | 8100  | 0.6552          | 0.7883   |
| 0.6671        | 37.0  | 8325  | 0.6511          | 0.7883   |
| 0.6452        | 38.0  | 8550  | 0.6474          | 0.7883   |
| 0.6514        | 39.0  | 8775  | 0.6441          | 0.7883   |
| 0.6682        | 40.0  | 9000  | 0.6412          | 0.7883   |
| 0.6552        | 41.0  | 9225  | 0.6385          | 0.7883   |
| 0.6851        | 42.0  | 9450  | 0.6362          | 0.79     |
| 0.6234        | 43.0  | 9675  | 0.6341          | 0.79     |
| 0.6753        | 44.0  | 9900  | 0.6324          | 0.79     |
| 0.6452        | 45.0  | 10125 | 0.6310          | 0.7917   |
| 0.6292        | 46.0  | 10350 | 0.6298          | 0.7917   |
| 0.6474        | 47.0  | 10575 | 0.6290          | 0.7917   |
| 0.6437        | 48.0  | 10800 | 0.6284          | 0.7917   |
| 0.6514        | 49.0  | 11025 | 0.6281          | 0.7917   |
| 0.6703        | 50.0  | 11250 | 0.6280          | 0.7917   |


### 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