
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_5x_beit_base_sgd_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: 1.1301
- Accuracy: 0.4017

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2159        | 1.0   | 375   | 1.3104          | 0.3133   |
| 1.2415        | 2.0   | 750   | 1.3020          | 0.3233   |
| 1.2057        | 3.0   | 1125  | 1.2939          | 0.3233   |
| 1.176         | 4.0   | 1500  | 1.2863          | 0.3267   |
| 1.2191        | 5.0   | 1875  | 1.2790          | 0.3267   |
| 1.1863        | 6.0   | 2250  | 1.2719          | 0.3333   |
| 1.2037        | 7.0   | 2625  | 1.2651          | 0.34     |
| 1.177         | 8.0   | 3000  | 1.2586          | 0.3483   |
| 1.1576        | 9.0   | 3375  | 1.2521          | 0.35     |
| 1.0865        | 10.0  | 3750  | 1.2459          | 0.3517   |
| 1.1578        | 11.0  | 4125  | 1.2399          | 0.3533   |
| 1.1516        | 12.0  | 4500  | 1.2341          | 0.355    |
| 1.1216        | 13.0  | 4875  | 1.2282          | 0.355    |
| 1.1365        | 14.0  | 5250  | 1.2228          | 0.3583   |
| 1.1282        | 15.0  | 5625  | 1.2175          | 0.3583   |
| 1.1187        | 16.0  | 6000  | 1.2123          | 0.3633   |
| 1.1048        | 17.0  | 6375  | 1.2074          | 0.365    |
| 1.1548        | 18.0  | 6750  | 1.2025          | 0.365    |
| 1.1271        | 19.0  | 7125  | 1.1978          | 0.3683   |
| 1.1003        | 20.0  | 7500  | 1.1934          | 0.3717   |
| 1.0771        | 21.0  | 7875  | 1.1891          | 0.3733   |
| 1.0833        | 22.0  | 8250  | 1.1849          | 0.3767   |
| 1.1002        | 23.0  | 8625  | 1.1809          | 0.3783   |
| 1.0994        | 24.0  | 9000  | 1.1772          | 0.3833   |
| 1.0715        | 25.0  | 9375  | 1.1735          | 0.385    |
| 1.1029        | 26.0  | 9750  | 1.1700          | 0.3867   |
| 1.1056        | 27.0  | 10125 | 1.1666          | 0.3867   |
| 1.022         | 28.0  | 10500 | 1.1633          | 0.3883   |
| 1.0343        | 29.0  | 10875 | 1.1602          | 0.3867   |
| 1.0325        | 30.0  | 11250 | 1.1573          | 0.3883   |
| 1.0378        | 31.0  | 11625 | 1.1546          | 0.3883   |
| 1.0659        | 32.0  | 12000 | 1.1519          | 0.3867   |
| 1.0282        | 33.0  | 12375 | 1.1495          | 0.3867   |
| 1.0519        | 34.0  | 12750 | 1.1472          | 0.3883   |
| 1.0399        | 35.0  | 13125 | 1.1451          | 0.3883   |
| 1.0632        | 36.0  | 13500 | 1.1430          | 0.39     |
| 1.015         | 37.0  | 13875 | 1.1411          | 0.39     |
| 1.0714        | 38.0  | 14250 | 1.1394          | 0.39     |
| 0.9921        | 39.0  | 14625 | 1.1379          | 0.3917   |
| 1.0391        | 40.0  | 15000 | 1.1365          | 0.3917   |
| 1.0121        | 41.0  | 15375 | 1.1352          | 0.395    |
| 1.0675        | 42.0  | 15750 | 1.1341          | 0.3967   |
| 1.0815        | 43.0  | 16125 | 1.1331          | 0.3967   |
| 1.0054        | 44.0  | 16500 | 1.1322          | 0.3967   |
| 1.0674        | 45.0  | 16875 | 1.1316          | 0.3983   |
| 1.0115        | 46.0  | 17250 | 1.1310          | 0.4      |
| 1.0426        | 47.0  | 17625 | 1.1306          | 0.4017   |
| 1.0416        | 48.0  | 18000 | 1.1303          | 0.4017   |
| 1.0297        | 49.0  | 18375 | 1.1302          | 0.4017   |
| 1.0431        | 50.0  | 18750 | 1.1301          | 0.4017   |


### 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