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# smids_1x_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: 0.6618
- Accuracy: 0.9032

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4246        | 1.0   | 76   | 0.3687          | 0.8598   |
| 0.2552        | 2.0   | 152  | 0.2999          | 0.8798   |
| 0.1978        | 3.0   | 228  | 0.2886          | 0.8731   |
| 0.1972        | 4.0   | 304  | 0.2763          | 0.8865   |
| 0.1608        | 5.0   | 380  | 0.2799          | 0.8865   |
| 0.1346        | 6.0   | 456  | 0.3048          | 0.8815   |
| 0.0943        | 7.0   | 532  | 0.3402          | 0.8898   |
| 0.0622        | 8.0   | 608  | 0.3287          | 0.8915   |
| 0.0613        | 9.0   | 684  | 0.3634          | 0.8865   |
| 0.0585        | 10.0  | 760  | 0.3905          | 0.8881   |
| 0.0328        | 11.0  | 836  | 0.3830          | 0.8948   |
| 0.0344        | 12.0  | 912  | 0.4094          | 0.8915   |
| 0.053         | 13.0  | 988  | 0.4103          | 0.8932   |
| 0.0261        | 14.0  | 1064 | 0.4498          | 0.8932   |
| 0.0261        | 15.0  | 1140 | 0.4936          | 0.8915   |
| 0.0343        | 16.0  | 1216 | 0.4859          | 0.8932   |
| 0.0153        | 17.0  | 1292 | 0.5143          | 0.8815   |
| 0.0038        | 18.0  | 1368 | 0.5271          | 0.8865   |
| 0.0046        | 19.0  | 1444 | 0.5417          | 0.8898   |
| 0.0282        | 20.0  | 1520 | 0.5283          | 0.8948   |
| 0.0048        | 21.0  | 1596 | 0.5421          | 0.8965   |
| 0.0018        | 22.0  | 1672 | 0.5503          | 0.8898   |
| 0.0064        | 23.0  | 1748 | 0.5860          | 0.8848   |
| 0.0241        | 24.0  | 1824 | 0.5762          | 0.8948   |
| 0.0207        | 25.0  | 1900 | 0.5869          | 0.8915   |
| 0.0293        | 26.0  | 1976 | 0.5842          | 0.8948   |
| 0.0029        | 27.0  | 2052 | 0.6141          | 0.8932   |
| 0.0198        | 28.0  | 2128 | 0.6046          | 0.8982   |
| 0.0329        | 29.0  | 2204 | 0.6286          | 0.8948   |
| 0.0036        | 30.0  | 2280 | 0.6053          | 0.8948   |
| 0.0339        | 31.0  | 2356 | 0.6159          | 0.8881   |
| 0.0211        | 32.0  | 2432 | 0.6253          | 0.8932   |
| 0.0315        | 33.0  | 2508 | 0.6357          | 0.8915   |
| 0.0135        | 34.0  | 2584 | 0.6365          | 0.8932   |
| 0.0361        | 35.0  | 2660 | 0.6309          | 0.8965   |
| 0.0313        | 36.0  | 2736 | 0.6365          | 0.8965   |
| 0.0198        | 37.0  | 2812 | 0.6348          | 0.8965   |
| 0.0132        | 38.0  | 2888 | 0.6243          | 0.8948   |
| 0.0085        | 39.0  | 2964 | 0.6351          | 0.8948   |
| 0.001         | 40.0  | 3040 | 0.6372          | 0.8948   |
| 0.0149        | 41.0  | 3116 | 0.6607          | 0.8998   |
| 0.0056        | 42.0  | 3192 | 0.6570          | 0.9065   |
| 0.0011        | 43.0  | 3268 | 0.6635          | 0.8998   |
| 0.003         | 44.0  | 3344 | 0.6527          | 0.8982   |
| 0.041         | 45.0  | 3420 | 0.6537          | 0.8982   |
| 0.0011        | 46.0  | 3496 | 0.6576          | 0.8982   |
| 0.0196        | 47.0  | 3572 | 0.6599          | 0.8998   |
| 0.0117        | 48.0  | 3648 | 0.6620          | 0.9032   |
| 0.0018        | 49.0  | 3724 | 0.6617          | 0.9032   |
| 0.0144        | 50.0  | 3800 | 0.6618          | 0.9032   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm