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# smids_3x_beit_base_adamax_00001_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: 0.7465
- Accuracy: 0.9083

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2292        | 1.0   | 375   | 0.3140          | 0.8683   |
| 0.2093        | 2.0   | 750   | 0.2336          | 0.9067   |
| 0.1226        | 3.0   | 1125  | 0.2477          | 0.9117   |
| 0.0876        | 4.0   | 1500  | 0.2419          | 0.92     |
| 0.0679        | 5.0   | 1875  | 0.3315          | 0.8983   |
| 0.0323        | 6.0   | 2250  | 0.3578          | 0.9033   |
| 0.0877        | 7.0   | 2625  | 0.4065          | 0.91     |
| 0.0394        | 8.0   | 3000  | 0.4499          | 0.91     |
| 0.0357        | 9.0   | 3375  | 0.4692          | 0.9167   |
| 0.0126        | 10.0  | 3750  | 0.5130          | 0.9083   |
| 0.0533        | 11.0  | 4125  | 0.4610          | 0.915    |
| 0.018         | 12.0  | 4500  | 0.5545          | 0.9167   |
| 0.0014        | 13.0  | 4875  | 0.6258          | 0.905    |
| 0.0294        | 14.0  | 5250  | 0.5991          | 0.9133   |
| 0.0147        | 15.0  | 5625  | 0.5948          | 0.91     |
| 0.0096        | 16.0  | 6000  | 0.6032          | 0.905    |
| 0.03          | 17.0  | 6375  | 0.6625          | 0.9017   |
| 0.0009        | 18.0  | 6750  | 0.6142          | 0.9067   |
| 0.0073        | 19.0  | 7125  | 0.7447          | 0.8917   |
| 0.0002        | 20.0  | 7500  | 0.6954          | 0.9067   |
| 0.0056        | 21.0  | 7875  | 0.7051          | 0.9133   |
| 0.0361        | 22.0  | 8250  | 0.6457          | 0.9067   |
| 0.0618        | 23.0  | 8625  | 0.6671          | 0.905    |
| 0.0003        | 24.0  | 9000  | 0.7426          | 0.905    |
| 0.0152        | 25.0  | 9375  | 0.6683          | 0.905    |
| 0.0005        | 26.0  | 9750  | 0.7087          | 0.9067   |
| 0.0002        | 27.0  | 10125 | 0.7480          | 0.905    |
| 0.004         | 28.0  | 10500 | 0.7511          | 0.905    |
| 0.0094        | 29.0  | 10875 | 0.7080          | 0.9017   |
| 0.0056        | 30.0  | 11250 | 0.7631          | 0.905    |
| 0.0441        | 31.0  | 11625 | 0.7580          | 0.9083   |
| 0.0024        | 32.0  | 12000 | 0.7682          | 0.9083   |
| 0.0002        | 33.0  | 12375 | 0.7496          | 0.9133   |
| 0.0005        | 34.0  | 12750 | 0.7614          | 0.9067   |
| 0.0057        | 35.0  | 13125 | 0.7635          | 0.9083   |
| 0.0004        | 36.0  | 13500 | 0.7425          | 0.9117   |
| 0.0153        | 37.0  | 13875 | 0.7300          | 0.91     |
| 0.0003        | 38.0  | 14250 | 0.7331          | 0.9083   |
| 0.0149        | 39.0  | 14625 | 0.7175          | 0.905    |
| 0.0093        | 40.0  | 15000 | 0.7444          | 0.9067   |
| 0.0001        | 41.0  | 15375 | 0.7317          | 0.9117   |
| 0.0           | 42.0  | 15750 | 0.7474          | 0.9033   |
| 0.0002        | 43.0  | 16125 | 0.7578          | 0.905    |
| 0.0004        | 44.0  | 16500 | 0.7636          | 0.905    |
| 0.0           | 45.0  | 16875 | 0.7676          | 0.91     |
| 0.0005        | 46.0  | 17250 | 0.7589          | 0.91     |
| 0.0158        | 47.0  | 17625 | 0.7484          | 0.9083   |
| 0.0001        | 48.0  | 18000 | 0.7568          | 0.91     |
| 0.0001        | 49.0  | 18375 | 0.7501          | 0.9083   |
| 0.0109        | 50.0  | 18750 | 0.7465          | 0.9083   |


### 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