
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_1x_beit_base_adamax_0001_fold3

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: 0.6695
- Accuracy: 0.9133

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3952        | 1.0   | 75   | 0.2900          | 0.885    |
| 0.2706        | 2.0   | 150  | 0.2858          | 0.895    |
| 0.1431        | 3.0   | 225  | 0.3509          | 0.8717   |
| 0.0811        | 4.0   | 300  | 0.2863          | 0.9183   |
| 0.0342        | 5.0   | 375  | 0.4874          | 0.8783   |
| 0.0334        | 6.0   | 450  | 0.4282          | 0.9117   |
| 0.0097        | 7.0   | 525  | 0.4216          | 0.92     |
| 0.0417        | 8.0   | 600  | 0.4392          | 0.91     |
| 0.0057        | 9.0   | 675  | 0.4243          | 0.9183   |
| 0.0023        | 10.0  | 750  | 0.5491          | 0.9      |
| 0.0342        | 11.0  | 825  | 0.4738          | 0.915    |
| 0.015         | 12.0  | 900  | 0.5105          | 0.9267   |
| 0.0179        | 13.0  | 975  | 0.6274          | 0.9083   |
| 0.0017        | 14.0  | 1050 | 0.5351          | 0.915    |
| 0.0012        | 15.0  | 1125 | 0.5446          | 0.905    |
| 0.0029        | 16.0  | 1200 | 0.5695          | 0.9067   |
| 0.0045        | 17.0  | 1275 | 0.5414          | 0.9133   |
| 0.0233        | 18.0  | 1350 | 0.7467          | 0.87     |
| 0.0001        | 19.0  | 1425 | 0.5934          | 0.9      |
| 0.0112        | 20.0  | 1500 | 0.5736          | 0.9067   |
| 0.0001        | 21.0  | 1575 | 0.6327          | 0.9033   |
| 0.0084        | 22.0  | 1650 | 0.5946          | 0.915    |
| 0.006         | 23.0  | 1725 | 0.5821          | 0.9133   |
| 0.0001        | 24.0  | 1800 | 0.6358          | 0.9      |
| 0.0           | 25.0  | 1875 | 0.5917          | 0.9117   |
| 0.0           | 26.0  | 1950 | 0.5998          | 0.9133   |
| 0.0002        | 27.0  | 2025 | 0.5967          | 0.915    |
| 0.0001        | 28.0  | 2100 | 0.5752          | 0.9117   |
| 0.0001        | 29.0  | 2175 | 0.6692          | 0.9      |
| 0.0044        | 30.0  | 2250 | 0.6493          | 0.9033   |
| 0.003         | 31.0  | 2325 | 0.6716          | 0.9117   |
| 0.0061        | 32.0  | 2400 | 0.7077          | 0.8983   |
| 0.0001        | 33.0  | 2475 | 0.6337          | 0.915    |
| 0.0003        | 34.0  | 2550 | 0.6698          | 0.9      |
| 0.0035        | 35.0  | 2625 | 0.6670          | 0.9033   |
| 0.0027        | 36.0  | 2700 | 0.6180          | 0.9067   |
| 0.0042        | 37.0  | 2775 | 0.6174          | 0.915    |
| 0.0023        | 38.0  | 2850 | 0.6161          | 0.9133   |
| 0.0001        | 39.0  | 2925 | 0.6601          | 0.91     |
| 0.0029        | 40.0  | 3000 | 0.6359          | 0.91     |
| 0.0           | 41.0  | 3075 | 0.6349          | 0.91     |
| 0.0022        | 42.0  | 3150 | 0.6576          | 0.9133   |
| 0.0028        | 43.0  | 3225 | 0.6662          | 0.9067   |
| 0.0           | 44.0  | 3300 | 0.6662          | 0.9083   |
| 0.0           | 45.0  | 3375 | 0.6797          | 0.9117   |
| 0.0           | 46.0  | 3450 | 0.6797          | 0.91     |
| 0.0043        | 47.0  | 3525 | 0.6738          | 0.91     |
| 0.0006        | 48.0  | 3600 | 0.6709          | 0.9133   |
| 0.0001        | 49.0  | 3675 | 0.6693          | 0.9133   |
| 0.0           | 50.0  | 3750 | 0.6695          | 0.9133   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm