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# smids_3x_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: 0.8301
- Accuracy: 0.9098

## 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  | 0.5124          | 0.9065   |
| 0.0367        | 11.0  | 4136  | 0.5327          | 0.9098   |
| 0.0236        | 12.0  | 4512  | 0.6380          | 0.8881   |
| 0.0306        | 13.0  | 4888  | 0.6670          | 0.9015   |
| 0.0605        | 14.0  | 5264  | 0.6155          | 0.9048   |
| 0.0306        | 15.0  | 5640  | 0.6494          | 0.9082   |
| 0.0004        | 16.0  | 6016  | 0.6896          | 0.9098   |
| 0.007         | 17.0  | 6392  | 0.7511          | 0.9082   |
| 0.015         | 18.0  | 6768  | 0.7380          | 0.9015   |
| 0.0089        | 19.0  | 7144  | 0.7967          | 0.8982   |
| 0.0001        | 20.0  | 7520  | 0.7574          | 0.9082   |
| 0.002         | 21.0  | 7896  | 0.7435          | 0.9098   |
| 0.0015        | 22.0  | 8272  | 0.7583          | 0.9115   |
| 0.0335        | 23.0  | 8648  | 0.7482          | 0.9132   |
| 0.0006        | 24.0  | 9024  | 0.7814          | 0.9098   |
| 0.0164        | 25.0  | 9400  | 0.7970          | 0.9065   |
| 0.0087        | 26.0  | 9776  | 0.7792          | 0.8948   |
| 0.0002        | 27.0  | 10152 | 0.7650          | 0.9032   |
| 0.0006        | 28.0  | 10528 | 0.7540          | 0.9115   |
| 0.0065        | 29.0  | 10904 | 0.7789          | 0.9048   |
| 0.0014        | 30.0  | 11280 | 0.8591          | 0.9015   |
| 0.0121        | 31.0  | 11656 | 0.7783          | 0.9149   |
| 0.0002        | 32.0  | 12032 | 0.7672          | 0.9132   |
| 0.0016        | 33.0  | 12408 | 0.7720          | 0.9132   |
| 0.0107        | 34.0  | 12784 | 0.7941          | 0.9132   |
| 0.0018        | 35.0  | 13160 | 0.8228          | 0.9132   |
| 0.0008        | 36.0  | 13536 | 0.8321          | 0.9082   |
| 0.0001        | 37.0  | 13912 | 0.8043          | 0.9149   |
| 0.0           | 38.0  | 14288 | 0.8040          | 0.9115   |
| 0.0001        | 39.0  | 14664 | 0.8027          | 0.9149   |
| 0.0057        | 40.0  | 15040 | 0.8085          | 0.9115   |
| 0.0           | 41.0  | 15416 | 0.8221          | 0.9082   |
| 0.0001        | 42.0  | 15792 | 0.8229          | 0.9115   |
| 0.0           | 43.0  | 16168 | 0.8358          | 0.9065   |
| 0.0001        | 44.0  | 16544 | 0.8054          | 0.9132   |
| 0.0104        | 45.0  | 16920 | 0.8285          | 0.9065   |
| 0.0009        | 46.0  | 17296 | 0.8368          | 0.9098   |
| 0.0003        | 47.0  | 17672 | 0.8285          | 0.9132   |
| 0.0028        | 48.0  | 18048 | 0.8334          | 0.9115   |
| 0.0156        | 49.0  | 18424 | 0.8298          | 0.9098   |
| 0.0004        | 50.0  | 18800 | 0.8301          | 0.9098   |


### 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