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# smids_5x_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: 0.6198
- Accuracy: 0.7145

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9679        | 1.0   | 376   | 0.9413          | 0.5025   |
| 0.8566        | 2.0   | 752   | 0.8816          | 0.5259   |
| 0.8176        | 3.0   | 1128  | 0.7970          | 0.5710   |
| 0.859         | 4.0   | 1504  | 0.8016          | 0.5626   |
| 1.031         | 5.0   | 1880  | 0.8469          | 0.5576   |
| 0.741         | 6.0   | 2256  | 0.8899          | 0.5426   |
| 0.7405        | 7.0   | 2632  | 0.8021          | 0.6194   |
| 0.7314        | 8.0   | 3008  | 0.7493          | 0.6327   |
| 0.6987        | 9.0   | 3384  | 0.7283          | 0.6461   |
| 0.7152        | 10.0  | 3760  | 0.7824          | 0.6260   |
| 0.6328        | 11.0  | 4136  | 0.7526          | 0.6294   |
| 0.8425        | 12.0  | 4512  | 0.7315          | 0.6678   |
| 0.7019        | 13.0  | 4888  | 0.7450          | 0.6344   |
| 0.695         | 14.0  | 5264  | 0.7358          | 0.6611   |
| 0.6413        | 15.0  | 5640  | 0.7306          | 0.6361   |
| 0.6189        | 16.0  | 6016  | 0.6963          | 0.6644   |
| 0.6245        | 17.0  | 6392  | 0.6845          | 0.6611   |
| 0.7409        | 18.0  | 6768  | 0.7226          | 0.6845   |
| 0.7054        | 19.0  | 7144  | 0.7324          | 0.6694   |
| 0.6703        | 20.0  | 7520  | 0.7291          | 0.6427   |
| 0.6526        | 21.0  | 7896  | 0.7119          | 0.6678   |
| 0.6212        | 22.0  | 8272  | 0.7262          | 0.6628   |
| 0.681         | 23.0  | 8648  | 0.6972          | 0.6644   |
| 0.6987        | 24.0  | 9024  | 0.7456          | 0.6594   |
| 0.6922        | 25.0  | 9400  | 0.6847          | 0.6694   |
| 0.6394        | 26.0  | 9776  | 0.6840          | 0.6745   |
| 0.6161        | 27.0  | 10152 | 0.6631          | 0.6828   |
| 0.5613        | 28.0  | 10528 | 0.6637          | 0.6761   |
| 0.6083        | 29.0  | 10904 | 0.7192          | 0.6745   |
| 0.6653        | 30.0  | 11280 | 0.6777          | 0.7045   |
| 0.5903        | 31.0  | 11656 | 0.6722          | 0.7012   |
| 0.6548        | 32.0  | 12032 | 0.7012          | 0.6511   |
| 0.5854        | 33.0  | 12408 | 0.6634          | 0.6811   |
| 0.614         | 34.0  | 12784 | 0.6595          | 0.6878   |
| 0.5441        | 35.0  | 13160 | 0.6831          | 0.6878   |
| 0.6051        | 36.0  | 13536 | 0.6864          | 0.6895   |
| 0.5008        | 37.0  | 13912 | 0.6407          | 0.6962   |
| 0.6049        | 38.0  | 14288 | 0.6571          | 0.7028   |
| 0.5851        | 39.0  | 14664 | 0.6506          | 0.6928   |
| 0.6223        | 40.0  | 15040 | 0.6528          | 0.6912   |
| 0.5754        | 41.0  | 15416 | 0.6469          | 0.7028   |
| 0.5456        | 42.0  | 15792 | 0.6395          | 0.7112   |
| 0.5001        | 43.0  | 16168 | 0.6344          | 0.7162   |
| 0.547         | 44.0  | 16544 | 0.6296          | 0.7212   |
| 0.4783        | 45.0  | 16920 | 0.6245          | 0.7179   |
| 0.4972        | 46.0  | 17296 | 0.6191          | 0.7312   |
| 0.5348        | 47.0  | 17672 | 0.6318          | 0.7295   |
| 0.5739        | 48.0  | 18048 | 0.6151          | 0.7062   |
| 0.4956        | 49.0  | 18424 | 0.6143          | 0.7179   |
| 0.4645        | 50.0  | 18800 | 0.6198          | 0.7145   |


### 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