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# smids_3x_beit_base_adamax_001_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: 1.1371
- Accuracy: 0.8817

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4638        | 1.0   | 225   | 0.4831          | 0.8      |
| 0.3593        | 2.0   | 450   | 0.4972          | 0.8017   |
| 0.3188        | 3.0   | 675   | 0.4598          | 0.8167   |
| 0.3007        | 4.0   | 900   | 0.3652          | 0.8567   |
| 0.1782        | 5.0   | 1125  | 0.4943          | 0.8467   |
| 0.2023        | 6.0   | 1350  | 0.3456          | 0.8833   |
| 0.1823        | 7.0   | 1575  | 0.4718          | 0.8517   |
| 0.2135        | 8.0   | 1800  | 0.4289          | 0.8333   |
| 0.1901        | 9.0   | 2025  | 0.3868          | 0.8767   |
| 0.1186        | 10.0  | 2250  | 0.5005          | 0.8567   |
| 0.1591        | 11.0  | 2475  | 0.4399          | 0.8633   |
| 0.1322        | 12.0  | 2700  | 0.4503          | 0.88     |
| 0.1689        | 13.0  | 2925  | 0.5822          | 0.855    |
| 0.1287        | 14.0  | 3150  | 0.5651          | 0.8533   |
| 0.0522        | 15.0  | 3375  | 0.6382          | 0.875    |
| 0.0395        | 16.0  | 3600  | 0.6522          | 0.88     |
| 0.0429        | 17.0  | 3825  | 0.6980          | 0.875    |
| 0.0661        | 18.0  | 4050  | 0.7096          | 0.855    |
| 0.0192        | 19.0  | 4275  | 0.7298          | 0.8733   |
| 0.0389        | 20.0  | 4500  | 0.7458          | 0.875    |
| 0.0026        | 21.0  | 4725  | 0.7349          | 0.88     |
| 0.0037        | 22.0  | 4950  | 0.8178          | 0.8833   |
| 0.006         | 23.0  | 5175  | 0.9683          | 0.8667   |
| 0.0578        | 24.0  | 5400  | 0.7875          | 0.8817   |
| 0.049         | 25.0  | 5625  | 0.7063          | 0.87     |
| 0.0011        | 26.0  | 5850  | 0.8220          | 0.8717   |
| 0.0006        | 27.0  | 6075  | 0.7462          | 0.8833   |
| 0.0152        | 28.0  | 6300  | 0.8411          | 0.8817   |
| 0.0204        | 29.0  | 6525  | 0.9258          | 0.875    |
| 0.0002        | 30.0  | 6750  | 0.8705          | 0.8683   |
| 0.0004        | 31.0  | 6975  | 0.8382          | 0.88     |
| 0.0001        | 32.0  | 7200  | 0.8871          | 0.8767   |
| 0.0069        | 33.0  | 7425  | 0.6754          | 0.8983   |
| 0.0062        | 34.0  | 7650  | 0.7823          | 0.8983   |
| 0.003         | 35.0  | 7875  | 0.8358          | 0.8883   |
| 0.0           | 36.0  | 8100  | 0.9463          | 0.885    |
| 0.0144        | 37.0  | 8325  | 1.0937          | 0.8717   |
| 0.0078        | 38.0  | 8550  | 1.0295          | 0.8867   |
| 0.0           | 39.0  | 8775  | 1.0240          | 0.8883   |
| 0.0001        | 40.0  | 9000  | 1.0443          | 0.8833   |
| 0.0           | 41.0  | 9225  | 1.0675          | 0.8933   |
| 0.0           | 42.0  | 9450  | 1.1657          | 0.8767   |
| 0.0035        | 43.0  | 9675  | 1.1312          | 0.8717   |
| 0.0           | 44.0  | 9900  | 1.1156          | 0.885    |
| 0.0           | 45.0  | 10125 | 1.1160          | 0.8817   |
| 0.0           | 46.0  | 10350 | 1.1325          | 0.8833   |
| 0.0008        | 47.0  | 10575 | 1.1407          | 0.8817   |
| 0.0           | 48.0  | 10800 | 1.1472          | 0.88     |
| 0.0001        | 49.0  | 11025 | 1.1387          | 0.8817   |
| 0.0           | 50.0  | 11250 | 1.1371          | 0.8817   |


### 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