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# smids_3x_beit_base_rms_00001_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: 0.9210
- 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2705        | 1.0   | 225   | 0.2918          | 0.8867   |
| 0.1226        | 2.0   | 450   | 0.2558          | 0.9033   |
| 0.1086        | 3.0   | 675   | 0.3279          | 0.9017   |
| 0.0616        | 4.0   | 900   | 0.4060          | 0.9      |
| 0.004         | 5.0   | 1125  | 0.5441          | 0.8967   |
| 0.0253        | 6.0   | 1350  | 0.5043          | 0.91     |
| 0.1141        | 7.0   | 1575  | 0.5368          | 0.9083   |
| 0.0284        | 8.0   | 1800  | 0.6779          | 0.8917   |
| 0.0461        | 9.0   | 2025  | 0.6024          | 0.9067   |
| 0.0081        | 10.0  | 2250  | 0.7134          | 0.8983   |
| 0.0002        | 11.0  | 2475  | 0.6405          | 0.9083   |
| 0.0399        | 12.0  | 2700  | 0.7188          | 0.9017   |
| 0.0307        | 13.0  | 2925  | 0.8188          | 0.8983   |
| 0.0003        | 14.0  | 3150  | 0.8326          | 0.8967   |
| 0.0034        | 15.0  | 3375  | 0.8891          | 0.895    |
| 0.063         | 16.0  | 3600  | 0.7795          | 0.8933   |
| 0.0282        | 17.0  | 3825  | 0.9130          | 0.9      |
| 0.0319        | 18.0  | 4050  | 0.8993          | 0.91     |
| 0.0347        | 19.0  | 4275  | 0.8296          | 0.9033   |
| 0.0001        | 20.0  | 4500  | 1.0072          | 0.895    |
| 0.0006        | 21.0  | 4725  | 0.8448          | 0.9067   |
| 0.0067        | 22.0  | 4950  | 0.8063          | 0.9017   |
| 0.0           | 23.0  | 5175  | 0.8686          | 0.905    |
| 0.0081        | 24.0  | 5400  | 0.9096          | 0.905    |
| 0.0036        | 25.0  | 5625  | 0.9376          | 0.9083   |
| 0.0003        | 26.0  | 5850  | 0.8940          | 0.9017   |
| 0.0002        | 27.0  | 6075  | 1.0157          | 0.9017   |
| 0.0           | 28.0  | 6300  | 0.9199          | 0.9033   |
| 0.0087        | 29.0  | 6525  | 0.8828          | 0.9133   |
| 0.0028        | 30.0  | 6750  | 0.9260          | 0.9083   |
| 0.0022        | 31.0  | 6975  | 0.8719          | 0.9183   |
| 0.0           | 32.0  | 7200  | 0.9025          | 0.9083   |
| 0.0039        | 33.0  | 7425  | 0.8825          | 0.9017   |
| 0.0           | 34.0  | 7650  | 0.8973          | 0.91     |
| 0.0019        | 35.0  | 7875  | 0.9103          | 0.9083   |
| 0.0           | 36.0  | 8100  | 0.9383          | 0.9033   |
| 0.0067        | 37.0  | 8325  | 1.0089          | 0.8983   |
| 0.0           | 38.0  | 8550  | 0.9107          | 0.9067   |
| 0.0004        | 39.0  | 8775  | 0.9825          | 0.9133   |
| 0.0           | 40.0  | 9000  | 1.0136          | 0.9017   |
| 0.0           | 41.0  | 9225  | 0.9657          | 0.905    |
| 0.0           | 42.0  | 9450  | 0.9670          | 0.9033   |
| 0.0042        | 43.0  | 9675  | 0.9419          | 0.9117   |
| 0.0           | 44.0  | 9900  | 0.9412          | 0.9083   |
| 0.0           | 45.0  | 10125 | 0.9113          | 0.91     |
| 0.0           | 46.0  | 10350 | 0.9674          | 0.9      |
| 0.0           | 47.0  | 10575 | 0.9600          | 0.9033   |
| 0.0           | 48.0  | 10800 | 0.9470          | 0.9083   |
| 0.015         | 49.0  | 11025 | 0.9211          | 0.9067   |
| 0.0           | 50.0  | 11250 | 0.9210          | 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