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# smids_5x_beit_base_adamax_0001_fold5

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: 1.0130
- Accuracy: 0.9067

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2609        | 1.0   | 375   | 0.4392          | 0.83     |
| 0.2435        | 2.0   | 750   | 0.3135          | 0.87     |
| 0.1555        | 3.0   | 1125  | 0.3719          | 0.885    |
| 0.1553        | 4.0   | 1500  | 0.4465          | 0.89     |
| 0.1           | 5.0   | 1875  | 0.4697          | 0.88     |
| 0.0953        | 6.0   | 2250  | 0.5131          | 0.895    |
| 0.1058        | 7.0   | 2625  | 0.5042          | 0.9033   |
| 0.0095        | 8.0   | 3000  | 0.6171          | 0.9033   |
| 0.0607        | 9.0   | 3375  | 0.5787          | 0.89     |
| 0.0225        | 10.0  | 3750  | 0.6004          | 0.8983   |
| 0.0522        | 11.0  | 4125  | 0.6713          | 0.89     |
| 0.0354        | 12.0  | 4500  | 0.8122          | 0.9      |
| 0.0281        | 13.0  | 4875  | 0.7547          | 0.8833   |
| 0.0538        | 14.0  | 5250  | 0.6866          | 0.88     |
| 0.0498        | 15.0  | 5625  | 0.7040          | 0.8783   |
| 0.0034        | 16.0  | 6000  | 0.6946          | 0.8883   |
| 0.0375        | 17.0  | 6375  | 0.7067          | 0.88     |
| 0.0372        | 18.0  | 6750  | 0.8461          | 0.875    |
| 0.0081        | 19.0  | 7125  | 0.5733          | 0.9      |
| 0.025         | 20.0  | 7500  | 0.6029          | 0.9167   |
| 0.0105        | 21.0  | 7875  | 0.6183          | 0.885    |
| 0.0342        | 22.0  | 8250  | 0.5174          | 0.9217   |
| 0.0291        | 23.0  | 8625  | 0.5708          | 0.9083   |
| 0.0316        | 24.0  | 9000  | 0.7866          | 0.8833   |
| 0.0002        | 25.0  | 9375  | 0.8031          | 0.895    |
| 0.0548        | 26.0  | 9750  | 0.7954          | 0.8933   |
| 0.0233        | 27.0  | 10125 | 0.8188          | 0.895    |
| 0.0003        | 28.0  | 10500 | 0.7997          | 0.9      |
| 0.0063        | 29.0  | 10875 | 0.8708          | 0.89     |
| 0.0025        | 30.0  | 11250 | 0.8386          | 0.8967   |
| 0.0008        | 31.0  | 11625 | 0.8998          | 0.8833   |
| 0.0           | 32.0  | 12000 | 0.9085          | 0.8967   |
| 0.0005        | 33.0  | 12375 | 0.7875          | 0.905    |
| 0.0           | 34.0  | 12750 | 0.9329          | 0.8983   |
| 0.0001        | 35.0  | 13125 | 0.7985          | 0.9017   |
| 0.0           | 36.0  | 13500 | 0.8234          | 0.8983   |
| 0.0           | 37.0  | 13875 | 0.8947          | 0.9033   |
| 0.005         | 38.0  | 14250 | 0.9096          | 0.9067   |
| 0.0291        | 39.0  | 14625 | 0.9293          | 0.9117   |
| 0.0006        | 40.0  | 15000 | 0.8881          | 0.9117   |
| 0.0           | 41.0  | 15375 | 1.0854          | 0.8967   |
| 0.0003        | 42.0  | 15750 | 0.9486          | 0.8983   |
| 0.0           | 43.0  | 16125 | 0.9324          | 0.91     |
| 0.0           | 44.0  | 16500 | 0.9408          | 0.9083   |
| 0.0           | 45.0  | 16875 | 1.0069          | 0.9067   |
| 0.0           | 46.0  | 17250 | 1.0803          | 0.9      |
| 0.013         | 47.0  | 17625 | 1.0261          | 0.905    |
| 0.0           | 48.0  | 18000 | 1.0163          | 0.9067   |
| 0.0           | 49.0  | 18375 | 1.0208          | 0.9083   |
| 0.0021        | 50.0  | 18750 | 1.0130          | 0.9067   |


### 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