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# smids_3x_beit_base_rms_00001_fold4

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: 1.2428
- Accuracy: 0.8833

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2439        | 1.0   | 225   | 0.3063          | 0.88     |
| 0.0996        | 2.0   | 450   | 0.3661          | 0.8767   |
| 0.0787        | 3.0   | 675   | 0.4587          | 0.8667   |
| 0.0663        | 4.0   | 900   | 0.5189          | 0.8733   |
| 0.0442        | 5.0   | 1125  | 0.7230          | 0.8683   |
| 0.049         | 6.0   | 1350  | 0.6529          | 0.885    |
| 0.0264        | 7.0   | 1575  | 0.8061          | 0.8817   |
| 0.0229        | 8.0   | 1800  | 0.7781          | 0.89     |
| 0.0395        | 9.0   | 2025  | 0.9069          | 0.88     |
| 0.0034        | 10.0  | 2250  | 0.8845          | 0.885    |
| 0.0389        | 11.0  | 2475  | 1.0336          | 0.8783   |
| 0.0025        | 12.0  | 2700  | 0.9857          | 0.8867   |
| 0.0142        | 13.0  | 2925  | 1.0341          | 0.885    |
| 0.0196        | 14.0  | 3150  | 1.1721          | 0.8767   |
| 0.0094        | 15.0  | 3375  | 1.0615          | 0.8767   |
| 0.0053        | 16.0  | 3600  | 1.1359          | 0.8767   |
| 0.0019        | 17.0  | 3825  | 1.1838          | 0.88     |
| 0.0236        | 18.0  | 4050  | 1.3731          | 0.8617   |
| 0.0037        | 19.0  | 4275  | 1.2473          | 0.8683   |
| 0.0001        | 20.0  | 4500  | 1.1836          | 0.8833   |
| 0.0008        | 21.0  | 4725  | 1.2284          | 0.8733   |
| 0.0           | 22.0  | 4950  | 1.1971          | 0.8867   |
| 0.015         | 23.0  | 5175  | 1.2985          | 0.8783   |
| 0.0           | 24.0  | 5400  | 1.3191          | 0.8683   |
| 0.0386        | 25.0  | 5625  | 1.3376          | 0.88     |
| 0.0001        | 26.0  | 5850  | 1.3273          | 0.8717   |
| 0.0019        | 27.0  | 6075  | 1.3269          | 0.8683   |
| 0.0001        | 28.0  | 6300  | 1.3093          | 0.8733   |
| 0.0           | 29.0  | 6525  | 1.2247          | 0.88     |
| 0.0           | 30.0  | 6750  | 1.2682          | 0.8733   |
| 0.0           | 31.0  | 6975  | 1.2123          | 0.8833   |
| 0.0           | 32.0  | 7200  | 1.2162          | 0.885    |
| 0.0027        | 33.0  | 7425  | 1.2786          | 0.8783   |
| 0.0           | 34.0  | 7650  | 1.3256          | 0.8817   |
| 0.0286        | 35.0  | 7875  | 1.2152          | 0.89     |
| 0.0           | 36.0  | 8100  | 1.2207          | 0.8833   |
| 0.0001        | 37.0  | 8325  | 1.2285          | 0.885    |
| 0.0004        | 38.0  | 8550  | 1.1956          | 0.89     |
| 0.0           | 39.0  | 8775  | 1.1853          | 0.8867   |
| 0.0401        | 40.0  | 9000  | 1.1341          | 0.8967   |
| 0.0           | 41.0  | 9225  | 1.1526          | 0.8883   |
| 0.0032        | 42.0  | 9450  | 1.1907          | 0.8817   |
| 0.0002        | 43.0  | 9675  | 1.2154          | 0.8833   |
| 0.0022        | 44.0  | 9900  | 1.1934          | 0.8833   |
| 0.0           | 45.0  | 10125 | 1.2765          | 0.88     |
| 0.0           | 46.0  | 10350 | 1.2545          | 0.8767   |
| 0.0002        | 47.0  | 10575 | 1.2393          | 0.8817   |
| 0.0           | 48.0  | 10800 | 1.2475          | 0.8817   |
| 0.0           | 49.0  | 11025 | 1.2453          | 0.8817   |
| 0.0           | 50.0  | 11250 | 1.2428          | 0.8833   |


### 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