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# smids_3x_beit_base_adamax_0001_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: 1.2272
- Accuracy: 0.8733

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3102        | 1.0   | 225   | 0.3679          | 0.8633   |
| 0.1053        | 2.0   | 450   | 0.3867          | 0.87     |
| 0.0809        | 3.0   | 675   | 0.4978          | 0.8617   |
| 0.1418        | 4.0   | 900   | 0.5585          | 0.8717   |
| 0.0152        | 5.0   | 1125  | 0.6419          | 0.885    |
| 0.0232        | 6.0   | 1350  | 0.6902          | 0.8717   |
| 0.0119        | 7.0   | 1575  | 0.8503          | 0.8633   |
| 0.0116        | 8.0   | 1800  | 0.8413          | 0.8667   |
| 0.0484        | 9.0   | 2025  | 0.9018          | 0.8683   |
| 0.0101        | 10.0  | 2250  | 0.9930          | 0.855    |
| 0.0039        | 11.0  | 2475  | 1.0769          | 0.8733   |
| 0.0004        | 12.0  | 2700  | 1.0602          | 0.8717   |
| 0.0292        | 13.0  | 2925  | 1.1584          | 0.875    |
| 0.0029        | 14.0  | 3150  | 1.2271          | 0.8583   |
| 0.01          | 15.0  | 3375  | 1.1632          | 0.8733   |
| 0.0001        | 16.0  | 3600  | 1.1832          | 0.8633   |
| 0.0           | 17.0  | 3825  | 1.2281          | 0.86     |
| 0.004         | 18.0  | 4050  | 1.0844          | 0.8783   |
| 0.0003        | 19.0  | 4275  | 1.2463          | 0.8683   |
| 0.0112        | 20.0  | 4500  | 1.2122          | 0.8733   |
| 0.0013        | 21.0  | 4725  | 1.2444          | 0.8617   |
| 0.0002        | 22.0  | 4950  | 1.2159          | 0.86     |
| 0.0002        | 23.0  | 5175  | 1.2215          | 0.8667   |
| 0.0           | 24.0  | 5400  | 1.2014          | 0.8733   |
| 0.0007        | 25.0  | 5625  | 1.1844          | 0.875    |
| 0.0           | 26.0  | 5850  | 1.3054          | 0.8683   |
| 0.0           | 27.0  | 6075  | 1.3588          | 0.8583   |
| 0.0332        | 28.0  | 6300  | 1.2029          | 0.875    |
| 0.0           | 29.0  | 6525  | 1.2414          | 0.87     |
| 0.0001        | 30.0  | 6750  | 1.2400          | 0.8783   |
| 0.0005        | 31.0  | 6975  | 1.1861          | 0.87     |
| 0.0           | 32.0  | 7200  | 1.1528          | 0.8767   |
| 0.0           | 33.0  | 7425  | 1.2071          | 0.8783   |
| 0.0002        | 34.0  | 7650  | 1.2652          | 0.875    |
| 0.0           | 35.0  | 7875  | 1.2647          | 0.8783   |
| 0.0           | 36.0  | 8100  | 1.3389          | 0.865    |
| 0.0           | 37.0  | 8325  | 1.3158          | 0.8683   |
| 0.0           | 38.0  | 8550  | 1.2845          | 0.8717   |
| 0.0           | 39.0  | 8775  | 1.2211          | 0.8783   |
| 0.0383        | 40.0  | 9000  | 1.3005          | 0.865    |
| 0.0001        | 41.0  | 9225  | 1.3129          | 0.8567   |
| 0.0025        | 42.0  | 9450  | 1.2924          | 0.865    |
| 0.0           | 43.0  | 9675  | 1.2393          | 0.8667   |
| 0.0021        | 44.0  | 9900  | 1.2861          | 0.87     |
| 0.0           | 45.0  | 10125 | 1.2626          | 0.8717   |
| 0.0           | 46.0  | 10350 | 1.2383          | 0.8733   |
| 0.0           | 47.0  | 10575 | 1.2652          | 0.8717   |
| 0.0           | 48.0  | 10800 | 1.2466          | 0.8733   |
| 0.0           | 49.0  | 11025 | 1.2259          | 0.875    |
| 0.0           | 50.0  | 11250 | 1.2272          | 0.8733   |


### 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