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# smids_1x_beit_base_adamax_0001_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: 0.8043
- 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3546        | 1.0   | 76   | 0.4865          | 0.7930   |
| 0.2399        | 2.0   | 152  | 0.3349          | 0.8731   |
| 0.136         | 3.0   | 228  | 0.2999          | 0.8831   |
| 0.1619        | 4.0   | 304  | 0.4346          | 0.8698   |
| 0.1213        | 5.0   | 380  | 0.4295          | 0.8748   |
| 0.0741        | 6.0   | 456  | 0.4439          | 0.8881   |
| 0.0995        | 7.0   | 532  | 0.5033          | 0.8815   |
| 0.0126        | 8.0   | 608  | 0.4887          | 0.8982   |
| 0.0174        | 9.0   | 684  | 0.6241          | 0.8848   |
| 0.0036        | 10.0  | 760  | 0.5630          | 0.8898   |
| 0.0047        | 11.0  | 836  | 0.6256          | 0.8898   |
| 0.025         | 12.0  | 912  | 0.5949          | 0.8982   |
| 0.0037        | 13.0  | 988  | 0.6192          | 0.8898   |
| 0.0095        | 14.0  | 1064 | 0.6191          | 0.8982   |
| 0.0074        | 15.0  | 1140 | 0.6693          | 0.8948   |
| 0.0061        | 16.0  | 1216 | 0.6785          | 0.8915   |
| 0.0003        | 17.0  | 1292 | 0.6825          | 0.8898   |
| 0.0001        | 18.0  | 1368 | 0.7695          | 0.8865   |
| 0.0107        | 19.0  | 1444 | 0.6909          | 0.8965   |
| 0.0125        | 20.0  | 1520 | 0.7272          | 0.8915   |
| 0.0016        | 21.0  | 1596 | 0.7585          | 0.8848   |
| 0.0028        | 22.0  | 1672 | 0.7524          | 0.8898   |
| 0.0017        | 23.0  | 1748 | 0.8165          | 0.8865   |
| 0.0046        | 24.0  | 1824 | 0.7698          | 0.8848   |
| 0.004         | 25.0  | 1900 | 0.8060          | 0.8915   |
| 0.003         | 26.0  | 1976 | 0.7525          | 0.8998   |
| 0.0039        | 27.0  | 2052 | 0.8271          | 0.8848   |
| 0.0001        | 28.0  | 2128 | 0.7809          | 0.8965   |
| 0.0001        | 29.0  | 2204 | 0.8142          | 0.8948   |
| 0.0           | 30.0  | 2280 | 0.7973          | 0.8881   |
| 0.0023        | 31.0  | 2356 | 0.7501          | 0.8998   |
| 0.0061        | 32.0  | 2432 | 0.7903          | 0.8932   |
| 0.0085        | 33.0  | 2508 | 0.7939          | 0.8932   |
| 0.0036        | 34.0  | 2584 | 0.7959          | 0.8982   |
| 0.0089        | 35.0  | 2660 | 0.7729          | 0.8982   |
| 0.0           | 36.0  | 2736 | 0.8000          | 0.8948   |
| 0.0038        | 37.0  | 2812 | 0.7757          | 0.8998   |
| 0.0028        | 38.0  | 2888 | 0.7902          | 0.8898   |
| 0.0024        | 39.0  | 2964 | 0.7785          | 0.9048   |
| 0.0001        | 40.0  | 3040 | 0.7668          | 0.9082   |
| 0.0052        | 41.0  | 3116 | 0.7725          | 0.9048   |
| 0.0           | 42.0  | 3192 | 0.7888          | 0.9032   |
| 0.0           | 43.0  | 3268 | 0.7934          | 0.9032   |
| 0.0           | 44.0  | 3344 | 0.7962          | 0.9032   |
| 0.0053        | 45.0  | 3420 | 0.8046          | 0.9032   |
| 0.0           | 46.0  | 3496 | 0.7994          | 0.9032   |
| 0.003         | 47.0  | 3572 | 0.8008          | 0.9032   |
| 0.0032        | 48.0  | 3648 | 0.8023          | 0.9032   |
| 0.0018        | 49.0  | 3724 | 0.8041          | 0.9032   |
| 0.0052        | 50.0  | 3800 | 0.8043          | 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