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# smids_5x_beit_base_rms_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: 1.0211
- Accuracy: 0.905

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3069        | 1.0   | 375   | 0.3920          | 0.8783   |
| 0.2324        | 2.0   | 750   | 0.2994          | 0.8883   |
| 0.2111        | 3.0   | 1125  | 0.4025          | 0.8883   |
| 0.1469        | 4.0   | 1500  | 0.4730          | 0.8933   |
| 0.1348        | 5.0   | 1875  | 0.5021          | 0.8667   |
| 0.1083        | 6.0   | 2250  | 0.5547          | 0.875    |
| 0.074         | 7.0   | 2625  | 0.8070          | 0.865    |
| 0.0264        | 8.0   | 3000  | 0.6666          | 0.8817   |
| 0.0566        | 9.0   | 3375  | 0.5845          | 0.8817   |
| 0.0498        | 10.0  | 3750  | 0.6165          | 0.8717   |
| 0.0562        | 11.0  | 4125  | 0.6616          | 0.9017   |
| 0.0419        | 12.0  | 4500  | 0.5768          | 0.9      |
| 0.042         | 13.0  | 4875  | 0.6169          | 0.89     |
| 0.0428        | 14.0  | 5250  | 0.6006          | 0.8967   |
| 0.065         | 15.0  | 5625  | 0.6268          | 0.875    |
| 0.0169        | 16.0  | 6000  | 0.6699          | 0.9017   |
| 0.0201        | 17.0  | 6375  | 0.7528          | 0.8933   |
| 0.0241        | 18.0  | 6750  | 0.6629          | 0.89     |
| 0.0027        | 19.0  | 7125  | 0.6425          | 0.9017   |
| 0.0221        | 20.0  | 7500  | 0.6769          | 0.8917   |
| 0.0018        | 21.0  | 7875  | 0.8187          | 0.8867   |
| 0.0303        | 22.0  | 8250  | 0.6653          | 0.8933   |
| 0.0112        | 23.0  | 8625  | 0.7146          | 0.88     |
| 0.002         | 24.0  | 9000  | 0.7847          | 0.8983   |
| 0.0001        | 25.0  | 9375  | 0.7706          | 0.8933   |
| 0.001         | 26.0  | 9750  | 0.8815          | 0.8933   |
| 0.0089        | 27.0  | 10125 | 0.9055          | 0.8833   |
| 0.0011        | 28.0  | 10500 | 0.8721          | 0.8883   |
| 0.0031        | 29.0  | 10875 | 0.8475          | 0.8917   |
| 0.0096        | 30.0  | 11250 | 0.7033          | 0.9067   |
| 0.0084        | 31.0  | 11625 | 0.7845          | 0.9033   |
| 0.0003        | 32.0  | 12000 | 0.8241          | 0.8967   |
| 0.0002        | 33.0  | 12375 | 0.7939          | 0.905    |
| 0.0           | 34.0  | 12750 | 0.8492          | 0.9117   |
| 0.0039        | 35.0  | 13125 | 0.7919          | 0.905    |
| 0.0           | 36.0  | 13500 | 0.9132          | 0.9017   |
| 0.001         | 37.0  | 13875 | 0.9026          | 0.91     |
| 0.0073        | 38.0  | 14250 | 0.9238          | 0.9      |
| 0.0           | 39.0  | 14625 | 1.0700          | 0.895    |
| 0.0           | 40.0  | 15000 | 1.0185          | 0.9083   |
| 0.0           | 41.0  | 15375 | 1.0113          | 0.9      |
| 0.0           | 42.0  | 15750 | 0.9606          | 0.9033   |
| 0.0           | 43.0  | 16125 | 1.0356          | 0.9      |
| 0.0           | 44.0  | 16500 | 1.0382          | 0.9017   |
| 0.0           | 45.0  | 16875 | 1.0522          | 0.9      |
| 0.0           | 46.0  | 17250 | 1.0733          | 0.8967   |
| 0.0031        | 47.0  | 17625 | 1.0418          | 0.9017   |
| 0.0           | 48.0  | 18000 | 1.0244          | 0.9067   |
| 0.0           | 49.0  | 18375 | 1.0206          | 0.905    |
| 0.0019        | 50.0  | 18750 | 1.0211          | 0.905    |


### 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