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# smids_3x_beit_base_adamax_001_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: 0.9992
- Accuracy: 0.8852

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6049        | 1.0   | 225   | 0.5478          | 0.7787   |
| 0.4381        | 2.0   | 450   | 0.4409          | 0.8270   |
| 0.378         | 3.0   | 675   | 0.4173          | 0.8386   |
| 0.3898        | 4.0   | 900   | 0.3437          | 0.8636   |
| 0.2572        | 5.0   | 1125  | 0.5400          | 0.8153   |
| 0.2909        | 6.0   | 1350  | 0.3922          | 0.8686   |
| 0.2559        | 7.0   | 1575  | 0.3276          | 0.8669   |
| 0.1836        | 8.0   | 1800  | 0.4262          | 0.8536   |
| 0.1786        | 9.0   | 2025  | 0.4524          | 0.8652   |
| 0.114         | 10.0  | 2250  | 0.4178          | 0.8652   |
| 0.1999        | 11.0  | 2475  | 0.4725          | 0.8619   |
| 0.0816        | 12.0  | 2700  | 0.4168          | 0.8802   |
| 0.1006        | 13.0  | 2925  | 0.4871          | 0.8636   |
| 0.0621        | 14.0  | 3150  | 0.5045          | 0.8486   |
| 0.1207        | 15.0  | 3375  | 0.5359          | 0.8735   |
| 0.1626        | 16.0  | 3600  | 0.5831          | 0.8586   |
| 0.0687        | 17.0  | 3825  | 0.5917          | 0.8702   |
| 0.0286        | 18.0  | 4050  | 0.6265          | 0.8819   |
| 0.0163        | 19.0  | 4275  | 0.5886          | 0.8752   |
| 0.0426        | 20.0  | 4500  | 0.5976          | 0.8686   |
| 0.0173        | 21.0  | 4725  | 0.5968          | 0.8819   |
| 0.0472        | 22.0  | 4950  | 0.8302          | 0.8586   |
| 0.0232        | 23.0  | 5175  | 0.7287          | 0.8769   |
| 0.0189        | 24.0  | 5400  | 0.6779          | 0.8686   |
| 0.0355        | 25.0  | 5625  | 0.7090          | 0.8802   |
| 0.0055        | 26.0  | 5850  | 0.7826          | 0.8769   |
| 0.003         | 27.0  | 6075  | 0.6780          | 0.8752   |
| 0.0006        | 28.0  | 6300  | 0.8190          | 0.8652   |
| 0.0008        | 29.0  | 6525  | 0.8233          | 0.8602   |
| 0.01          | 30.0  | 6750  | 0.8980          | 0.8552   |
| 0.0041        | 31.0  | 6975  | 0.9765          | 0.8552   |
| 0.002         | 32.0  | 7200  | 0.9007          | 0.8619   |
| 0.0055        | 33.0  | 7425  | 0.9545          | 0.8735   |
| 0.0034        | 34.0  | 7650  | 0.8622          | 0.8719   |
| 0.0003        | 35.0  | 7875  | 0.9467          | 0.8785   |
| 0.0042        | 36.0  | 8100  | 0.8925          | 0.8819   |
| 0.0003        | 37.0  | 8325  | 0.9807          | 0.8802   |
| 0.0           | 38.0  | 8550  | 1.0504          | 0.8769   |
| 0.0067        | 39.0  | 8775  | 1.0615          | 0.8752   |
| 0.0017        | 40.0  | 9000  | 1.0987          | 0.8719   |
| 0.0011        | 41.0  | 9225  | 1.0223          | 0.8835   |
| 0.0001        | 42.0  | 9450  | 0.9989          | 0.8852   |
| 0.0           | 43.0  | 9675  | 0.9839          | 0.8852   |
| 0.0           | 44.0  | 9900  | 1.0002          | 0.8802   |
| 0.0           | 45.0  | 10125 | 0.9842          | 0.8869   |
| 0.0002        | 46.0  | 10350 | 0.9885          | 0.8885   |
| 0.0           | 47.0  | 10575 | 0.9981          | 0.8869   |
| 0.0           | 48.0  | 10800 | 0.9986          | 0.8852   |
| 0.0051        | 49.0  | 11025 | 0.9988          | 0.8835   |
| 0.0023        | 50.0  | 11250 | 0.9992          | 0.8852   |


### 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