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# smids_3x_beit_base_rms_00001_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: 0.7714
- Accuracy: 0.93

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2858        | 1.0   | 225   | 0.2465          | 0.9067   |
| 0.1768        | 2.0   | 450   | 0.2541          | 0.9117   |
| 0.0881        | 3.0   | 675   | 0.2719          | 0.9133   |
| 0.0505        | 4.0   | 900   | 0.3645          | 0.9083   |
| 0.0569        | 5.0   | 1125  | 0.3912          | 0.9117   |
| 0.0143        | 6.0   | 1350  | 0.5093          | 0.91     |
| 0.0377        | 7.0   | 1575  | 0.6703          | 0.895    |
| 0.0322        | 8.0   | 1800  | 0.5423          | 0.9067   |
| 0.0082        | 9.0   | 2025  | 0.5861          | 0.92     |
| 0.0276        | 10.0  | 2250  | 0.6888          | 0.9133   |
| 0.0302        | 11.0  | 2475  | 0.6182          | 0.92     |
| 0.0237        | 12.0  | 2700  | 0.7796          | 0.9067   |
| 0.006         | 13.0  | 2925  | 0.6569          | 0.9217   |
| 0.0168        | 14.0  | 3150  | 0.6640          | 0.925    |
| 0.0004        | 15.0  | 3375  | 0.7467          | 0.9083   |
| 0.0142        | 16.0  | 3600  | 0.8289          | 0.9033   |
| 0.001         | 17.0  | 3825  | 0.7332          | 0.9183   |
| 0.0           | 18.0  | 4050  | 0.7402          | 0.9167   |
| 0.0125        | 19.0  | 4275  | 0.7537          | 0.9183   |
| 0.0043        | 20.0  | 4500  | 0.7046          | 0.9233   |
| 0.0           | 21.0  | 4725  | 0.7969          | 0.9067   |
| 0.0052        | 22.0  | 4950  | 0.7422          | 0.9217   |
| 0.0           | 23.0  | 5175  | 0.7848          | 0.9083   |
| 0.0005        | 24.0  | 5400  | 0.8567          | 0.9133   |
| 0.0301        | 25.0  | 5625  | 0.7666          | 0.9267   |
| 0.0039        | 26.0  | 5850  | 0.7330          | 0.9217   |
| 0.0001        | 27.0  | 6075  | 0.7599          | 0.9233   |
| 0.0002        | 28.0  | 6300  | 0.8806          | 0.9083   |
| 0.0061        | 29.0  | 6525  | 0.7763          | 0.92     |
| 0.004         | 30.0  | 6750  | 0.8161          | 0.9117   |
| 0.0022        | 31.0  | 6975  | 0.8369          | 0.9217   |
| 0.0           | 32.0  | 7200  | 0.7564          | 0.9283   |
| 0.0005        | 33.0  | 7425  | 0.7872          | 0.9283   |
| 0.0001        | 34.0  | 7650  | 0.8316          | 0.9183   |
| 0.0001        | 35.0  | 7875  | 0.8563          | 0.915    |
| 0.0           | 36.0  | 8100  | 0.7792          | 0.9267   |
| 0.0           | 37.0  | 8325  | 0.8267          | 0.9217   |
| 0.0           | 38.0  | 8550  | 0.8036          | 0.9233   |
| 0.0003        | 39.0  | 8775  | 0.8623          | 0.9217   |
| 0.0           | 40.0  | 9000  | 0.8053          | 0.9167   |
| 0.0004        | 41.0  | 9225  | 0.7926          | 0.93     |
| 0.0           | 42.0  | 9450  | 0.7751          | 0.9267   |
| 0.0           | 43.0  | 9675  | 0.8025          | 0.9267   |
| 0.0           | 44.0  | 9900  | 0.7634          | 0.925    |
| 0.0           | 45.0  | 10125 | 0.7768          | 0.9283   |
| 0.0175        | 46.0  | 10350 | 0.8501          | 0.92     |
| 0.0025        | 47.0  | 10575 | 0.7670          | 0.93     |
| 0.0003        | 48.0  | 10800 | 0.7741          | 0.925    |
| 0.0           | 49.0  | 11025 | 0.7719          | 0.9283   |
| 0.0001        | 50.0  | 11250 | 0.7714          | 0.93     |


### 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