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# smids_5x_beit_base_adamax_00001_fold3

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: 0.7903
- Accuracy: 0.9133

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2779        | 1.0   | 375   | 0.3054          | 0.8733   |
| 0.2162        | 2.0   | 750   | 0.2359          | 0.92     |
| 0.1285        | 3.0   | 1125  | 0.2539          | 0.9217   |
| 0.0945        | 4.0   | 1500  | 0.2722          | 0.9233   |
| 0.101         | 5.0   | 1875  | 0.3077          | 0.92     |
| 0.0634        | 6.0   | 2250  | 0.3559          | 0.915    |
| 0.0294        | 7.0   | 2625  | 0.3959          | 0.9167   |
| 0.0374        | 8.0   | 3000  | 0.4694          | 0.9117   |
| 0.0228        | 9.0   | 3375  | 0.4761          | 0.9217   |
| 0.0104        | 10.0  | 3750  | 0.5190          | 0.925    |
| 0.0112        | 11.0  | 4125  | 0.5753          | 0.9183   |
| 0.0112        | 12.0  | 4500  | 0.5854          | 0.92     |
| 0.0095        | 13.0  | 4875  | 0.6151          | 0.9117   |
| 0.015         | 14.0  | 5250  | 0.6292          | 0.91     |
| 0.0156        | 15.0  | 5625  | 0.6403          | 0.9167   |
| 0.0162        | 16.0  | 6000  | 0.6509          | 0.9183   |
| 0.0041        | 17.0  | 6375  | 0.6568          | 0.9183   |
| 0.0184        | 18.0  | 6750  | 0.6884          | 0.915    |
| 0.0009        | 19.0  | 7125  | 0.7500          | 0.9083   |
| 0.0067        | 20.0  | 7500  | 0.7058          | 0.9183   |
| 0.0003        | 21.0  | 7875  | 0.6969          | 0.915    |
| 0.0024        | 22.0  | 8250  | 0.7459          | 0.915    |
| 0.0561        | 23.0  | 8625  | 0.6852          | 0.9183   |
| 0.0018        | 24.0  | 9000  | 0.6779          | 0.9233   |
| 0.0005        | 25.0  | 9375  | 0.7388          | 0.9183   |
| 0.0019        | 26.0  | 9750  | 0.7333          | 0.9217   |
| 0.0279        | 27.0  | 10125 | 0.7591          | 0.9133   |
| 0.0247        | 28.0  | 10500 | 0.7516          | 0.92     |
| 0.0017        | 29.0  | 10875 | 0.7698          | 0.9183   |
| 0.0267        | 30.0  | 11250 | 0.7550          | 0.9183   |
| 0.0002        | 31.0  | 11625 | 0.7870          | 0.9167   |
| 0.0313        | 32.0  | 12000 | 0.7814          | 0.9133   |
| 0.0004        | 33.0  | 12375 | 0.7692          | 0.915    |
| 0.0293        | 34.0  | 12750 | 0.7888          | 0.915    |
| 0.0001        | 35.0  | 13125 | 0.7707          | 0.915    |
| 0.0004        | 36.0  | 13500 | 0.7961          | 0.915    |
| 0.0001        | 37.0  | 13875 | 0.7594          | 0.9133   |
| 0.0           | 38.0  | 14250 | 0.7716          | 0.92     |
| 0.0004        | 39.0  | 14625 | 0.7795          | 0.92     |
| 0.0058        | 40.0  | 15000 | 0.7828          | 0.915    |
| 0.0005        | 41.0  | 15375 | 0.7680          | 0.9117   |
| 0.0001        | 42.0  | 15750 | 0.7737          | 0.915    |
| 0.0001        | 43.0  | 16125 | 0.7805          | 0.915    |
| 0.003         | 44.0  | 16500 | 0.7912          | 0.915    |
| 0.0003        | 45.0  | 16875 | 0.7859          | 0.9167   |
| 0.0104        | 46.0  | 17250 | 0.7879          | 0.9167   |
| 0.0006        | 47.0  | 17625 | 0.7848          | 0.915    |
| 0.0           | 48.0  | 18000 | 0.7845          | 0.915    |
| 0.0           | 49.0  | 18375 | 0.7887          | 0.915    |
| 0.0           | 50.0  | 18750 | 0.7903          | 0.9133   |


### 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