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# smids_5x_beit_base_rms_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: 0.7105
- Accuracy: 0.7396

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1123        | 1.0   | 376   | 1.1251          | 0.3356   |
| 0.9352        | 2.0   | 752   | 0.8920          | 0.5259   |
| 0.794         | 3.0   | 1128  | 0.7965          | 0.5526   |
| 0.8608        | 4.0   | 1504  | 0.8108          | 0.5643   |
| 0.8805        | 5.0   | 1880  | 0.7967          | 0.5543   |
| 0.7654        | 6.0   | 2256  | 0.8049          | 0.5693   |
| 0.7409        | 7.0   | 2632  | 0.7866          | 0.5726   |
| 0.7913        | 8.0   | 3008  | 0.7897          | 0.5893   |
| 0.7475        | 9.0   | 3384  | 0.7603          | 0.6127   |
| 0.7457        | 10.0  | 3760  | 0.7604          | 0.5810   |
| 0.6765        | 11.0  | 4136  | 0.7505          | 0.6060   |
| 0.7009        | 12.0  | 4512  | 0.7036          | 0.6628   |
| 0.682         | 13.0  | 4888  | 0.7131          | 0.6477   |
| 0.665         | 14.0  | 5264  | 0.7097          | 0.6628   |
| 0.6075        | 15.0  | 5640  | 0.7283          | 0.6361   |
| 0.6104        | 16.0  | 6016  | 0.7252          | 0.6845   |
| 0.5756        | 17.0  | 6392  | 0.7019          | 0.6761   |
| 0.6308        | 18.0  | 6768  | 0.7144          | 0.6678   |
| 0.6215        | 19.0  | 7144  | 0.6989          | 0.6644   |
| 0.5991        | 20.0  | 7520  | 0.6580          | 0.7262   |
| 0.644         | 21.0  | 7896  | 0.6509          | 0.7129   |
| 0.5561        | 22.0  | 8272  | 0.6219          | 0.7112   |
| 0.5743        | 23.0  | 8648  | 0.6259          | 0.7095   |
| 0.5779        | 24.0  | 9024  | 0.7360          | 0.6611   |
| 0.5703        | 25.0  | 9400  | 0.7402          | 0.6544   |
| 0.59          | 26.0  | 9776  | 0.6505          | 0.7179   |
| 0.4484        | 27.0  | 10152 | 0.7061          | 0.6945   |
| 0.5078        | 28.0  | 10528 | 0.6625          | 0.7012   |
| 0.4947        | 29.0  | 10904 | 0.7197          | 0.6878   |
| 0.4804        | 30.0  | 11280 | 0.6601          | 0.7129   |
| 0.571         | 31.0  | 11656 | 0.6610          | 0.6978   |
| 0.5506        | 32.0  | 12032 | 0.6726          | 0.7012   |
| 0.4066        | 33.0  | 12408 | 0.6633          | 0.7095   |
| 0.4713        | 34.0  | 12784 | 0.6198          | 0.7245   |
| 0.4603        | 35.0  | 13160 | 0.6655          | 0.7145   |
| 0.4936        | 36.0  | 13536 | 0.6620          | 0.7212   |
| 0.4422        | 37.0  | 13912 | 0.6199          | 0.7446   |
| 0.4404        | 38.0  | 14288 | 0.6881          | 0.7062   |
| 0.4643        | 39.0  | 14664 | 0.6209          | 0.7412   |
| 0.4403        | 40.0  | 15040 | 0.6524          | 0.7496   |
| 0.4197        | 41.0  | 15416 | 0.6575          | 0.7229   |
| 0.3846        | 42.0  | 15792 | 0.6496          | 0.7295   |
| 0.3794        | 43.0  | 16168 | 0.6583          | 0.7179   |
| 0.4461        | 44.0  | 16544 | 0.6644          | 0.7329   |
| 0.3616        | 45.0  | 16920 | 0.6911          | 0.7396   |
| 0.3764        | 46.0  | 17296 | 0.7023          | 0.7279   |
| 0.39          | 47.0  | 17672 | 0.6999          | 0.7379   |
| 0.3595        | 48.0  | 18048 | 0.7003          | 0.7379   |
| 0.3678        | 49.0  | 18424 | 0.6974          | 0.7379   |
| 0.2726        | 50.0  | 18800 | 0.7105          | 0.7396   |


### 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