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# smids_3x_beit_base_rms_0001_fold2

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: 1.0800
- Accuracy: 0.9002

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4112        | 1.0   | 225   | 0.4131          | 0.8602   |
| 0.2912        | 2.0   | 450   | 0.4363          | 0.8369   |
| 0.1579        | 3.0   | 675   | 0.3616          | 0.8752   |
| 0.187         | 4.0   | 900   | 0.2854          | 0.8852   |
| 0.1222        | 5.0   | 1125  | 0.4884          | 0.8835   |
| 0.0818        | 6.0   | 1350  | 0.4361          | 0.8885   |
| 0.0981        | 7.0   | 1575  | 0.4218          | 0.8769   |
| 0.1165        | 8.0   | 1800  | 0.5449          | 0.8702   |
| 0.0593        | 9.0   | 2025  | 0.5250          | 0.9002   |
| 0.0725        | 10.0  | 2250  | 0.5111          | 0.8985   |
| 0.0275        | 11.0  | 2475  | 0.5486          | 0.8702   |
| 0.0408        | 12.0  | 2700  | 0.6442          | 0.8852   |
| 0.0654        | 13.0  | 2925  | 0.6277          | 0.8968   |
| 0.0139        | 14.0  | 3150  | 0.6248          | 0.8918   |
| 0.0393        | 15.0  | 3375  | 0.5753          | 0.8935   |
| 0.0368        | 16.0  | 3600  | 0.6499          | 0.8902   |
| 0.0316        | 17.0  | 3825  | 0.6023          | 0.8918   |
| 0.0193        | 18.0  | 4050  | 0.7084          | 0.8952   |
| 0.001         | 19.0  | 4275  | 0.7253          | 0.9002   |
| 0.0578        | 20.0  | 4500  | 0.7248          | 0.8785   |
| 0.08          | 21.0  | 4725  | 0.6832          | 0.8902   |
| 0.0213        | 22.0  | 4950  | 0.8468          | 0.8902   |
| 0.008         | 23.0  | 5175  | 0.8669          | 0.8935   |
| 0.0041        | 24.0  | 5400  | 0.8402          | 0.8802   |
| 0.0205        | 25.0  | 5625  | 0.8106          | 0.8869   |
| 0.0196        | 26.0  | 5850  | 0.8576          | 0.8902   |
| 0.0001        | 27.0  | 6075  | 0.7352          | 0.8985   |
| 0.0003        | 28.0  | 6300  | 0.7339          | 0.9018   |
| 0.0078        | 29.0  | 6525  | 0.8497          | 0.8985   |
| 0.007         | 30.0  | 6750  | 1.0378          | 0.8802   |
| 0.0063        | 31.0  | 6975  | 0.9737          | 0.8902   |
| 0.0147        | 32.0  | 7200  | 0.9357          | 0.8902   |
| 0.0358        | 33.0  | 7425  | 0.9702          | 0.8885   |
| 0.0003        | 34.0  | 7650  | 0.7989          | 0.8902   |
| 0.0001        | 35.0  | 7875  | 0.9353          | 0.8885   |
| 0.0032        | 36.0  | 8100  | 0.8664          | 0.8952   |
| 0.0015        | 37.0  | 8325  | 0.7955          | 0.8968   |
| 0.0           | 38.0  | 8550  | 0.8664          | 0.8952   |
| 0.0222        | 39.0  | 8775  | 0.9521          | 0.8985   |
| 0.0042        | 40.0  | 9000  | 0.9427          | 0.8985   |
| 0.0027        | 41.0  | 9225  | 0.9502          | 0.9002   |
| 0.0           | 42.0  | 9450  | 1.0516          | 0.8935   |
| 0.0           | 43.0  | 9675  | 0.9695          | 0.8952   |
| 0.0           | 44.0  | 9900  | 1.0122          | 0.8985   |
| 0.0           | 45.0  | 10125 | 0.9974          | 0.8985   |
| 0.0           | 46.0  | 10350 | 1.0109          | 0.9002   |
| 0.0           | 47.0  | 10575 | 1.0770          | 0.8952   |
| 0.0           | 48.0  | 10800 | 1.0946          | 0.8985   |
| 0.0025        | 49.0  | 11025 | 1.0859          | 0.9002   |
| 0.002         | 50.0  | 11250 | 1.0800          | 0.9002   |


### 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