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# smids_5x_beit_base_rms_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: 1.3262
- Accuracy: 0.8217

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.931         | 1.0   | 375   | 0.8668          | 0.5083   |
| 0.8597        | 2.0   | 750   | 0.7892          | 0.6017   |
| 0.7587        | 3.0   | 1125  | 0.7350          | 0.6383   |
| 0.7046        | 4.0   | 1500  | 0.7282          | 0.65     |
| 0.6817        | 5.0   | 1875  | 0.7027          | 0.6567   |
| 0.6292        | 6.0   | 2250  | 0.6987          | 0.6683   |
| 0.6024        | 7.0   | 2625  | 0.5984          | 0.7267   |
| 0.6528        | 8.0   | 3000  | 0.5956          | 0.7267   |
| 0.5546        | 9.0   | 3375  | 0.5629          | 0.765    |
| 0.4767        | 10.0  | 3750  | 0.5576          | 0.75     |
| 0.4967        | 11.0  | 4125  | 0.4703          | 0.8017   |
| 0.3904        | 12.0  | 4500  | 0.4630          | 0.8083   |
| 0.395         | 13.0  | 4875  | 0.4837          | 0.8      |
| 0.4102        | 14.0  | 5250  | 0.4887          | 0.815    |
| 0.4425        | 15.0  | 5625  | 0.4472          | 0.8317   |
| 0.269         | 16.0  | 6000  | 0.4817          | 0.8133   |
| 0.3554        | 17.0  | 6375  | 0.4030          | 0.8483   |
| 0.3667        | 18.0  | 6750  | 0.4187          | 0.83     |
| 0.2943        | 19.0  | 7125  | 0.4575          | 0.8333   |
| 0.2361        | 20.0  | 7500  | 0.4670          | 0.8317   |
| 0.2672        | 21.0  | 7875  | 0.4447          | 0.8383   |
| 0.2065        | 22.0  | 8250  | 0.4671          | 0.8267   |
| 0.3036        | 23.0  | 8625  | 0.5659          | 0.8167   |
| 0.1998        | 24.0  | 9000  | 0.5359          | 0.8233   |
| 0.1813        | 25.0  | 9375  | 0.4898          | 0.85     |
| 0.16          | 26.0  | 9750  | 0.5701          | 0.835    |
| 0.1617        | 27.0  | 10125 | 0.5423          | 0.8333   |
| 0.1338        | 28.0  | 10500 | 0.5644          | 0.8483   |
| 0.1411        | 29.0  | 10875 | 0.5853          | 0.8267   |
| 0.0859        | 30.0  | 11250 | 0.6605          | 0.8217   |
| 0.101         | 31.0  | 11625 | 0.7234          | 0.8317   |
| 0.0828        | 32.0  | 12000 | 0.6563          | 0.8367   |
| 0.1039        | 33.0  | 12375 | 0.7913          | 0.82     |
| 0.0772        | 34.0  | 12750 | 0.8613          | 0.82     |
| 0.0737        | 35.0  | 13125 | 0.7477          | 0.8283   |
| 0.0714        | 36.0  | 13500 | 0.9064          | 0.83     |
| 0.0337        | 37.0  | 13875 | 0.8383          | 0.8367   |
| 0.094         | 38.0  | 14250 | 0.9398          | 0.8233   |
| 0.0203        | 39.0  | 14625 | 0.9121          | 0.8267   |
| 0.0289        | 40.0  | 15000 | 1.0830          | 0.8283   |
| 0.0242        | 41.0  | 15375 | 1.1069          | 0.825    |
| 0.0154        | 42.0  | 15750 | 1.1781          | 0.8117   |
| 0.009         | 43.0  | 16125 | 1.1755          | 0.8167   |
| 0.0144        | 44.0  | 16500 | 1.1730          | 0.8233   |
| 0.0239        | 45.0  | 16875 | 1.4682          | 0.8083   |
| 0.0221        | 46.0  | 17250 | 1.3105          | 0.82     |
| 0.0362        | 47.0  | 17625 | 1.3368          | 0.8317   |
| 0.0008        | 48.0  | 18000 | 1.2965          | 0.8317   |
| 0.0038        | 49.0  | 18375 | 1.2931          | 0.8317   |
| 0.0178        | 50.0  | 18750 | 1.3262          | 0.8217   |


### 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