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# smids_3x_beit_base_adamax_001_fold1

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9792
- Accuracy: 0.8848

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5347        | 1.0   | 226   | 0.5865          | 0.7796   |
| 0.481         | 2.0   | 452   | 0.4735          | 0.8047   |
| 0.392         | 3.0   | 678   | 0.3827          | 0.8397   |
| 0.3513        | 4.0   | 904   | 0.4550          | 0.8080   |
| 0.3191        | 5.0   | 1130  | 0.5279          | 0.8364   |
| 0.2659        | 6.0   | 1356  | 0.3980          | 0.8564   |
| 0.2461        | 7.0   | 1582  | 0.3991          | 0.8798   |
| 0.2656        | 8.0   | 1808  | 0.4588          | 0.8664   |
| 0.1595        | 9.0   | 2034  | 0.4089          | 0.8715   |
| 0.1456        | 10.0  | 2260  | 0.4772          | 0.8631   |
| 0.0575        | 11.0  | 2486  | 0.5294          | 0.8614   |
| 0.0953        | 12.0  | 2712  | 0.4940          | 0.8748   |
| 0.0784        | 13.0  | 2938  | 0.5992          | 0.8548   |
| 0.0313        | 14.0  | 3164  | 0.5155          | 0.8731   |
| 0.1006        | 15.0  | 3390  | 0.5131          | 0.8898   |
| 0.0394        | 16.0  | 3616  | 0.6916          | 0.8815   |
| 0.0372        | 17.0  | 3842  | 0.6693          | 0.8748   |
| 0.0368        | 18.0  | 4068  | 0.7021          | 0.8765   |
| 0.0584        | 19.0  | 4294  | 0.7487          | 0.8715   |
| 0.0031        | 20.0  | 4520  | 0.6697          | 0.8865   |
| 0.0088        | 21.0  | 4746  | 0.7746          | 0.8865   |
| 0.0319        | 22.0  | 4972  | 0.7417          | 0.8614   |
| 0.0133        | 23.0  | 5198  | 0.9026          | 0.8581   |
| 0.001         | 24.0  | 5424  | 0.7822          | 0.8865   |
| 0.0186        | 25.0  | 5650  | 0.8476          | 0.8698   |
| 0.0405        | 26.0  | 5876  | 0.7548          | 0.8915   |
| 0.0061        | 27.0  | 6102  | 0.7539          | 0.8798   |
| 0.0213        | 28.0  | 6328  | 0.8310          | 0.8848   |
| 0.0063        | 29.0  | 6554  | 0.7841          | 0.8781   |
| 0.0003        | 30.0  | 6780  | 0.8782          | 0.8798   |
| 0.0005        | 31.0  | 7006  | 0.8431          | 0.8865   |
| 0.0002        | 32.0  | 7232  | 0.8900          | 0.8915   |
| 0.0077        | 33.0  | 7458  | 0.9508          | 0.8898   |
| 0.0001        | 34.0  | 7684  | 0.8836          | 0.8848   |
| 0.0001        | 35.0  | 7910  | 0.8853          | 0.8898   |
| 0.0002        | 36.0  | 8136  | 0.8931          | 0.8865   |
| 0.0           | 37.0  | 8362  | 0.9183          | 0.8831   |
| 0.0           | 38.0  | 8588  | 0.9668          | 0.8865   |
| 0.0           | 39.0  | 8814  | 0.9612          | 0.8881   |
| 0.0002        | 40.0  | 9040  | 0.9819          | 0.8848   |
| 0.0033        | 41.0  | 9266  | 0.9561          | 0.8915   |
| 0.0038        | 42.0  | 9492  | 0.9632          | 0.8915   |
| 0.0001        | 43.0  | 9718  | 0.9739          | 0.8865   |
| 0.0           | 44.0  | 9944  | 0.9696          | 0.8848   |
| 0.0           | 45.0  | 10170 | 0.9928          | 0.8815   |
| 0.0           | 46.0  | 10396 | 0.9848          | 0.8798   |
| 0.0           | 47.0  | 10622 | 0.9849          | 0.8815   |
| 0.0           | 48.0  | 10848 | 0.9754          | 0.8831   |
| 0.0           | 49.0  | 11074 | 0.9791          | 0.8848   |
| 0.0           | 50.0  | 11300 | 0.9792          | 0.8848   |


### 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