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# smids_5x_beit_base_rms_00001_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: 0.9950
- Accuracy: 0.9052

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2499        | 1.0   | 375   | 0.2304          | 0.9185   |
| 0.1775        | 2.0   | 750   | 0.2550          | 0.9018   |
| 0.1217        | 3.0   | 1125  | 0.4013          | 0.8869   |
| 0.0433        | 4.0   | 1500  | 0.5189          | 0.8819   |
| 0.0358        | 5.0   | 1875  | 0.4893          | 0.8985   |
| 0.0375        | 6.0   | 2250  | 0.5725          | 0.9052   |
| 0.0405        | 7.0   | 2625  | 0.5904          | 0.9101   |
| 0.012         | 8.0   | 3000  | 0.7002          | 0.9018   |
| 0.0053        | 9.0   | 3375  | 0.7065          | 0.9052   |
| 0.0037        | 10.0  | 3750  | 0.7485          | 0.8985   |
| 0.0126        | 11.0  | 4125  | 0.7919          | 0.8985   |
| 0.0005        | 12.0  | 4500  | 0.7919          | 0.8968   |
| 0.0159        | 13.0  | 4875  | 0.8564          | 0.8902   |
| 0.0001        | 14.0  | 5250  | 0.8426          | 0.8852   |
| 0.0002        | 15.0  | 5625  | 0.8433          | 0.8918   |
| 0.0148        | 16.0  | 6000  | 0.7634          | 0.9018   |
| 0.0208        | 17.0  | 6375  | 0.8403          | 0.8952   |
| 0.0001        | 18.0  | 6750  | 0.8471          | 0.9018   |
| 0.0491        | 19.0  | 7125  | 0.8371          | 0.9035   |
| 0.0           | 20.0  | 7500  | 0.7423          | 0.9052   |
| 0.0126        | 21.0  | 7875  | 0.8759          | 0.8935   |
| 0.0008        | 22.0  | 8250  | 0.8648          | 0.9002   |
| 0.0           | 23.0  | 8625  | 0.9554          | 0.9002   |
| 0.0005        | 24.0  | 9000  | 0.9755          | 0.8918   |
| 0.0184        | 25.0  | 9375  | 0.9160          | 0.8918   |
| 0.0           | 26.0  | 9750  | 0.9691          | 0.8918   |
| 0.0           | 27.0  | 10125 | 0.8701          | 0.8968   |
| 0.0002        | 28.0  | 10500 | 0.7677          | 0.9035   |
| 0.0001        | 29.0  | 10875 | 0.9258          | 0.9035   |
| 0.0033        | 30.0  | 11250 | 0.9080          | 0.9002   |
| 0.0045        | 31.0  | 11625 | 1.0210          | 0.8935   |
| 0.0045        | 32.0  | 12000 | 0.9883          | 0.8985   |
| 0.0017        | 33.0  | 12375 | 0.8984          | 0.9035   |
| 0.0           | 34.0  | 12750 | 0.8844          | 0.9101   |
| 0.0007        | 35.0  | 13125 | 0.9085          | 0.8918   |
| 0.0002        | 36.0  | 13500 | 0.9790          | 0.9035   |
| 0.0           | 37.0  | 13875 | 1.0705          | 0.8985   |
| 0.0           | 38.0  | 14250 | 1.0172          | 0.9035   |
| 0.0           | 39.0  | 14625 | 1.0259          | 0.9052   |
| 0.0032        | 40.0  | 15000 | 1.0712          | 0.9018   |
| 0.0           | 41.0  | 15375 | 1.0107          | 0.9002   |
| 0.0025        | 42.0  | 15750 | 1.0002          | 0.9068   |
| 0.0023        | 43.0  | 16125 | 1.0032          | 0.9035   |
| 0.003         | 44.0  | 16500 | 0.9837          | 0.9052   |
| 0.0018        | 45.0  | 16875 | 1.0127          | 0.9035   |
| 0.0           | 46.0  | 17250 | 0.9843          | 0.9068   |
| 0.0056        | 47.0  | 17625 | 1.0283          | 0.9002   |
| 0.0033        | 48.0  | 18000 | 1.0135          | 0.9052   |
| 0.0031        | 49.0  | 18375 | 0.9997          | 0.9052   |
| 0.0025        | 50.0  | 18750 | 0.9950          | 0.9052   |


### 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