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# smids_5x_beit_base_rms_00001_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: 1.0277
- Accuracy: 0.8965

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.169         | 1.0   | 376   | 0.2845          | 0.8848   |
| 0.1527        | 2.0   | 752   | 0.2709          | 0.9132   |
| 0.1446        | 3.0   | 1128  | 0.3421          | 0.8998   |
| 0.0485        | 4.0   | 1504  | 0.4474          | 0.9065   |
| 0.0159        | 5.0   | 1880  | 0.4847          | 0.8965   |
| 0.0162        | 6.0   | 2256  | 0.6046          | 0.8982   |
| 0.0753        | 7.0   | 2632  | 0.6419          | 0.8898   |
| 0.0455        | 8.0   | 3008  | 0.7218          | 0.8965   |
| 0.0437        | 9.0   | 3384  | 0.8405          | 0.8815   |
| 0.007         | 10.0  | 3760  | 0.7349          | 0.9015   |
| 0.0254        | 11.0  | 4136  | 0.8461          | 0.8915   |
| 0.0214        | 12.0  | 4512  | 0.7638          | 0.8898   |
| 0.0283        | 13.0  | 4888  | 0.8735          | 0.8948   |
| 0.0331        | 14.0  | 5264  | 0.8577          | 0.8932   |
| 0.0029        | 15.0  | 5640  | 0.9013          | 0.8982   |
| 0.0041        | 16.0  | 6016  | 0.9992          | 0.8698   |
| 0.0007        | 17.0  | 6392  | 0.9147          | 0.8865   |
| 0.0019        | 18.0  | 6768  | 0.9339          | 0.8915   |
| 0.0002        | 19.0  | 7144  | 0.8625          | 0.8982   |
| 0.0341        | 20.0  | 7520  | 0.9287          | 0.8815   |
| 0.0           | 21.0  | 7896  | 1.0011          | 0.8831   |
| 0.0           | 22.0  | 8272  | 0.8805          | 0.8948   |
| 0.0028        | 23.0  | 8648  | 0.9347          | 0.8965   |
| 0.0001        | 24.0  | 9024  | 0.9930          | 0.8965   |
| 0.001         | 25.0  | 9400  | 1.0054          | 0.8982   |
| 0.029         | 26.0  | 9776  | 0.8994          | 0.8932   |
| 0.0028        | 27.0  | 10152 | 0.9209          | 0.8865   |
| 0.0009        | 28.0  | 10528 | 0.9409          | 0.8998   |
| 0.0018        | 29.0  | 10904 | 1.0441          | 0.8848   |
| 0.0163        | 30.0  | 11280 | 0.9017          | 0.9032   |
| 0.0           | 31.0  | 11656 | 0.8554          | 0.9015   |
| 0.0           | 32.0  | 12032 | 0.8702          | 0.9048   |
| 0.0001        | 33.0  | 12408 | 0.9551          | 0.8965   |
| 0.0           | 34.0  | 12784 | 0.9265          | 0.8982   |
| 0.0004        | 35.0  | 13160 | 1.0253          | 0.8865   |
| 0.0044        | 36.0  | 13536 | 0.9098          | 0.8948   |
| 0.0003        | 37.0  | 13912 | 0.9290          | 0.9032   |
| 0.0           | 38.0  | 14288 | 1.0072          | 0.8948   |
| 0.0           | 39.0  | 14664 | 1.0677          | 0.8948   |
| 0.0           | 40.0  | 15040 | 1.0064          | 0.8982   |
| 0.0           | 41.0  | 15416 | 0.9891          | 0.8982   |
| 0.0           | 42.0  | 15792 | 1.0628          | 0.8948   |
| 0.0           | 43.0  | 16168 | 1.0396          | 0.8915   |
| 0.0           | 44.0  | 16544 | 1.0033          | 0.8982   |
| 0.0           | 45.0  | 16920 | 1.0214          | 0.8998   |
| 0.0033        | 46.0  | 17296 | 1.0498          | 0.8898   |
| 0.0           | 47.0  | 17672 | 1.0375          | 0.8932   |
| 0.0           | 48.0  | 18048 | 1.0305          | 0.8898   |
| 0.0           | 49.0  | 18424 | 1.0285          | 0.8948   |
| 0.0028        | 50.0  | 18800 | 1.0277          | 0.8965   |


### 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