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# smids_5x_beit_base_adamax_001_fold3

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: 1.1469
- Accuracy: 0.835

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8795        | 1.0   | 375   | 1.0709          | 0.465    |
| 0.805         | 2.0   | 750   | 0.8267          | 0.54     |
| 0.7578        | 3.0   | 1125  | 0.8592          | 0.5683   |
| 1.0023        | 4.0   | 1500  | 0.7631          | 0.6317   |
| 0.7622        | 5.0   | 1875  | 0.6997          | 0.685    |
| 0.5711        | 6.0   | 2250  | 0.5607          | 0.76     |
| 0.5125        | 7.0   | 2625  | 0.4986          | 0.8067   |
| 0.5239        | 8.0   | 3000  | 0.4781          | 0.8      |
| 0.4547        | 9.0   | 3375  | 0.6145          | 0.77     |
| 0.4777        | 10.0  | 3750  | 0.4360          | 0.8267   |
| 0.3636        | 11.0  | 4125  | 0.4074          | 0.8417   |
| 0.4518        | 12.0  | 4500  | 0.4481          | 0.8317   |
| 0.3493        | 13.0  | 4875  | 0.5307          | 0.805    |
| 0.3009        | 14.0  | 5250  | 0.4470          | 0.835    |
| 0.2927        | 15.0  | 5625  | 0.4302          | 0.8383   |
| 0.233         | 16.0  | 6000  | 0.4642          | 0.835    |
| 0.3176        | 17.0  | 6375  | 0.4522          | 0.835    |
| 0.2704        | 18.0  | 6750  | 0.4353          | 0.8317   |
| 0.2544        | 19.0  | 7125  | 0.4509          | 0.835    |
| 0.2122        | 20.0  | 7500  | 0.5169          | 0.8183   |
| 0.135         | 21.0  | 7875  | 0.5912          | 0.82     |
| 0.1564        | 22.0  | 8250  | 0.4970          | 0.8383   |
| 0.2284        | 23.0  | 8625  | 0.5113          | 0.8217   |
| 0.1624        | 24.0  | 9000  | 0.6295          | 0.825    |
| 0.165         | 25.0  | 9375  | 0.5951          | 0.81     |
| 0.0933        | 26.0  | 9750  | 0.6337          | 0.8233   |
| 0.1787        | 27.0  | 10125 | 0.5739          | 0.8267   |
| 0.0977        | 28.0  | 10500 | 0.6837          | 0.8283   |
| 0.0607        | 29.0  | 10875 | 0.7084          | 0.8467   |
| 0.0515        | 30.0  | 11250 | 0.8096          | 0.8167   |
| 0.0587        | 31.0  | 11625 | 0.8299          | 0.8367   |
| 0.1097        | 32.0  | 12000 | 0.7487          | 0.8333   |
| 0.0784        | 33.0  | 12375 | 0.7788          | 0.815    |
| 0.0505        | 34.0  | 12750 | 0.8520          | 0.8417   |
| 0.0243        | 35.0  | 13125 | 0.8865          | 0.8233   |
| 0.0517        | 36.0  | 13500 | 0.8229          | 0.83     |
| 0.0484        | 37.0  | 13875 | 0.9870          | 0.8367   |
| 0.0198        | 38.0  | 14250 | 0.9718          | 0.825    |
| 0.0203        | 39.0  | 14625 | 0.8284          | 0.8467   |
| 0.0211        | 40.0  | 15000 | 0.9506          | 0.8333   |
| 0.0035        | 41.0  | 15375 | 0.9695          | 0.8367   |
| 0.0109        | 42.0  | 15750 | 1.1050          | 0.835    |
| 0.0054        | 43.0  | 16125 | 1.1815          | 0.8317   |
| 0.0043        | 44.0  | 16500 | 1.0406          | 0.8433   |
| 0.0242        | 45.0  | 16875 | 1.1360          | 0.8417   |
| 0.0127        | 46.0  | 17250 | 1.1706          | 0.8317   |
| 0.0068        | 47.0  | 17625 | 1.1596          | 0.8333   |
| 0.0108        | 48.0  | 18000 | 1.1303          | 0.8333   |
| 0.0029        | 49.0  | 18375 | 1.1332          | 0.8267   |
| 0.0113        | 50.0  | 18750 | 1.1469          | 0.835    |


### 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