
<!-- 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_adamax_00001_fold1

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: 0.8312
- Accuracy: 0.9065

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2349        | 1.0   | 376   | 0.2964          | 0.8848   |
| 0.2022        | 2.0   | 752   | 0.2944          | 0.8932   |
| 0.1706        | 3.0   | 1128  | 0.2893          | 0.8965   |
| 0.0767        | 4.0   | 1504  | 0.3105          | 0.9015   |
| 0.0646        | 5.0   | 1880  | 0.3471          | 0.9015   |
| 0.0505        | 6.0   | 2256  | 0.3777          | 0.9015   |
| 0.0505        | 7.0   | 2632  | 0.4146          | 0.9115   |
| 0.0821        | 8.0   | 3008  | 0.4739          | 0.9115   |
| 0.0331        | 9.0   | 3384  | 0.5133          | 0.9082   |
| 0.0097        | 10.0  | 3760  | 0.5125          | 0.9065   |
| 0.0368        | 11.0  | 4136  | 0.5327          | 0.9098   |
| 0.0236        | 12.0  | 4512  | 0.6377          | 0.8881   |
| 0.0306        | 13.0  | 4888  | 0.6671          | 0.9015   |
| 0.0605        | 14.0  | 5264  | 0.6154          | 0.9048   |
| 0.0306        | 15.0  | 5640  | 0.6497          | 0.9082   |
| 0.0004        | 16.0  | 6016  | 0.6905          | 0.9098   |
| 0.0062        | 17.0  | 6392  | 0.7456          | 0.9082   |
| 0.0157        | 18.0  | 6768  | 0.7362          | 0.9048   |
| 0.0117        | 19.0  | 7144  | 0.8082          | 0.8965   |
| 0.0001        | 20.0  | 7520  | 0.7613          | 0.9098   |
| 0.0049        | 21.0  | 7896  | 0.7376          | 0.9115   |
| 0.0013        | 22.0  | 8272  | 0.7490          | 0.9098   |
| 0.0339        | 23.0  | 8648  | 0.7577          | 0.9132   |
| 0.0009        | 24.0  | 9024  | 0.7847          | 0.9098   |
| 0.0161        | 25.0  | 9400  | 0.7983          | 0.9098   |
| 0.0079        | 26.0  | 9776  | 0.7734          | 0.8948   |
| 0.0004        | 27.0  | 10152 | 0.7368          | 0.9015   |
| 0.0005        | 28.0  | 10528 | 0.7478          | 0.9098   |
| 0.0059        | 29.0  | 10904 | 0.7755          | 0.9065   |
| 0.0012        | 30.0  | 11280 | 0.8338          | 0.9082   |
| 0.0142        | 31.0  | 11656 | 0.7783          | 0.9115   |
| 0.0002        | 32.0  | 12032 | 0.7615          | 0.9165   |
| 0.0004        | 33.0  | 12408 | 0.7711          | 0.9098   |
| 0.0127        | 34.0  | 12784 | 0.7865          | 0.9165   |
| 0.0032        | 35.0  | 13160 | 0.8207          | 0.9132   |
| 0.0006        | 36.0  | 13536 | 0.8174          | 0.9098   |
| 0.0001        | 37.0  | 13912 | 0.7992          | 0.9165   |
| 0.0           | 38.0  | 14288 | 0.8040          | 0.9082   |
| 0.0001        | 39.0  | 14664 | 0.8011          | 0.9132   |
| 0.0005        | 40.0  | 15040 | 0.8052          | 0.9115   |
| 0.0001        | 41.0  | 15416 | 0.8158          | 0.9082   |
| 0.0001        | 42.0  | 15792 | 0.8157          | 0.9098   |
| 0.0           | 43.0  | 16168 | 0.8347          | 0.9065   |
| 0.0004        | 44.0  | 16544 | 0.8096          | 0.9048   |
| 0.0087        | 45.0  | 16920 | 0.8231          | 0.9065   |
| 0.0003        | 46.0  | 17296 | 0.8362          | 0.9065   |
| 0.0002        | 47.0  | 17672 | 0.8291          | 0.9098   |
| 0.0046        | 48.0  | 18048 | 0.8341          | 0.9082   |
| 0.0134        | 49.0  | 18424 | 0.8309          | 0.9065   |
| 0.0004        | 50.0  | 18800 | 0.8312          | 0.9065   |


### 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