
<!-- 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_rms_001_fold4

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: 1.7051
- Accuracy: 0.7983

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7798        | 1.0   | 375   | 0.7672          | 0.5633   |
| 0.803         | 2.0   | 750   | 0.7688          | 0.58     |
| 0.7686        | 3.0   | 1125  | 0.7514          | 0.61     |
| 0.7375        | 4.0   | 1500  | 0.8350          | 0.5517   |
| 0.7507        | 5.0   | 1875  | 0.8001          | 0.595    |
| 0.7083        | 6.0   | 2250  | 0.7244          | 0.65     |
| 0.708         | 7.0   | 2625  | 0.7289          | 0.6467   |
| 0.7266        | 8.0   | 3000  | 0.7325          | 0.6633   |
| 0.6418        | 9.0   | 3375  | 0.6940          | 0.6917   |
| 0.673         | 10.0  | 3750  | 0.7042          | 0.6617   |
| 0.6803        | 11.0  | 4125  | 0.6907          | 0.6817   |
| 0.64          | 12.0  | 4500  | 0.6890          | 0.675    |
| 0.6467        | 13.0  | 4875  | 0.7095          | 0.67     |
| 0.6428        | 14.0  | 5250  | 0.6543          | 0.7083   |
| 0.6389        | 15.0  | 5625  | 0.5890          | 0.7383   |
| 0.5885        | 16.0  | 6000  | 0.5874          | 0.7383   |
| 0.5689        | 17.0  | 6375  | 0.6828          | 0.705    |
| 0.5988        | 18.0  | 6750  | 0.6153          | 0.74     |
| 0.5869        | 19.0  | 7125  | 0.5556          | 0.745    |
| 0.5829        | 20.0  | 7500  | 0.5816          | 0.7417   |
| 0.5202        | 21.0  | 7875  | 0.6299          | 0.7267   |
| 0.4671        | 22.0  | 8250  | 0.5955          | 0.7383   |
| 0.4713        | 23.0  | 8625  | 0.5489          | 0.7783   |
| 0.4814        | 24.0  | 9000  | 0.6063          | 0.76     |
| 0.4578        | 25.0  | 9375  | 0.6548          | 0.7367   |
| 0.4226        | 26.0  | 9750  | 0.5459          | 0.75     |
| 0.349         | 27.0  | 10125 | 0.6223          | 0.76     |
| 0.3499        | 28.0  | 10500 | 0.5682          | 0.7817   |
| 0.2869        | 29.0  | 10875 | 0.7135          | 0.7717   |
| 0.3419        | 30.0  | 11250 | 0.6094          | 0.7833   |
| 0.3402        | 31.0  | 11625 | 0.6473          | 0.785    |
| 0.3025        | 32.0  | 12000 | 0.6500          | 0.7783   |
| 0.2278        | 33.0  | 12375 | 0.7439          | 0.7633   |
| 0.2211        | 34.0  | 12750 | 0.7227          | 0.775    |
| 0.1813        | 35.0  | 13125 | 0.7187          | 0.8033   |
| 0.1887        | 36.0  | 13500 | 0.7980          | 0.7883   |
| 0.2308        | 37.0  | 13875 | 0.8180          | 0.8      |
| 0.1362        | 38.0  | 14250 | 0.8499          | 0.7867   |
| 0.1204        | 39.0  | 14625 | 0.8914          | 0.8033   |
| 0.1182        | 40.0  | 15000 | 0.9026          | 0.7933   |
| 0.1271        | 41.0  | 15375 | 1.1021          | 0.775    |
| 0.0646        | 42.0  | 15750 | 1.1489          | 0.7967   |
| 0.0428        | 43.0  | 16125 | 1.2387          | 0.8067   |
| 0.0277        | 44.0  | 16500 | 1.2320          | 0.81     |
| 0.0276        | 45.0  | 16875 | 1.3879          | 0.79     |
| 0.0246        | 46.0  | 17250 | 1.4881          | 0.8033   |
| 0.0344        | 47.0  | 17625 | 1.5278          | 0.7983   |
| 0.006         | 48.0  | 18000 | 1.5757          | 0.8017   |
| 0.0048        | 49.0  | 18375 | 1.6617          | 0.8033   |
| 0.0042        | 50.0  | 18750 | 1.7051          | 0.7983   |


### 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