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# smids_3x_beit_base_sgd_0001_fold5

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: 0.5470
- Accuracy: 0.7833

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.227         | 1.0   | 225   | 1.2590          | 0.3417   |
| 1.1831        | 2.0   | 450   | 1.2003          | 0.3717   |
| 1.084         | 3.0   | 675   | 1.1460          | 0.4017   |
| 1.0157        | 4.0   | 900   | 1.0941          | 0.42     |
| 0.9735        | 5.0   | 1125  | 1.0438          | 0.455    |
| 0.9436        | 6.0   | 1350  | 0.9975          | 0.4917   |
| 0.9179        | 7.0   | 1575  | 0.9541          | 0.5333   |
| 0.8784        | 8.0   | 1800  | 0.9154          | 0.5717   |
| 0.884         | 9.0   | 2025  | 0.8784          | 0.595    |
| 0.8367        | 10.0  | 2250  | 0.8446          | 0.6217   |
| 0.8299        | 11.0  | 2475  | 0.8151          | 0.6483   |
| 0.7845        | 12.0  | 2700  | 0.7879          | 0.6617   |
| 0.7765        | 13.0  | 2925  | 0.7642          | 0.6667   |
| 0.7469        | 14.0  | 3150  | 0.7428          | 0.6783   |
| 0.7257        | 15.0  | 3375  | 0.7247          | 0.69     |
| 0.6997        | 16.0  | 3600  | 0.7073          | 0.7      |
| 0.7213        | 17.0  | 3825  | 0.6920          | 0.7083   |
| 0.698         | 18.0  | 4050  | 0.6780          | 0.7183   |
| 0.7064        | 19.0  | 4275  | 0.6649          | 0.7217   |
| 0.6988        | 20.0  | 4500  | 0.6533          | 0.735    |
| 0.6396        | 21.0  | 4725  | 0.6426          | 0.7383   |
| 0.6558        | 22.0  | 4950  | 0.6328          | 0.7483   |
| 0.6628        | 23.0  | 5175  | 0.6239          | 0.75     |
| 0.6417        | 24.0  | 5400  | 0.6165          | 0.7533   |
| 0.6414        | 25.0  | 5625  | 0.6079          | 0.7517   |
| 0.6773        | 26.0  | 5850  | 0.6018          | 0.7567   |
| 0.662         | 27.0  | 6075  | 0.5968          | 0.7583   |
| 0.6119        | 28.0  | 6300  | 0.5913          | 0.765    |
| 0.6058        | 29.0  | 6525  | 0.5864          | 0.765    |
| 0.5469        | 30.0  | 6750  | 0.5816          | 0.7683   |
| 0.6085        | 31.0  | 6975  | 0.5777          | 0.7667   |
| 0.557         | 32.0  | 7200  | 0.5744          | 0.7667   |
| 0.5975        | 33.0  | 7425  | 0.5708          | 0.7683   |
| 0.5747        | 34.0  | 7650  | 0.5675          | 0.7717   |
| 0.6075        | 35.0  | 7875  | 0.5645          | 0.7717   |
| 0.5661        | 36.0  | 8100  | 0.5618          | 0.7733   |
| 0.5862        | 37.0  | 8325  | 0.5597          | 0.7733   |
| 0.5867        | 38.0  | 8550  | 0.5581          | 0.775    |
| 0.5414        | 39.0  | 8775  | 0.5562          | 0.7767   |
| 0.5431        | 40.0  | 9000  | 0.5546          | 0.775    |
| 0.5693        | 41.0  | 9225  | 0.5532          | 0.7767   |
| 0.5499        | 42.0  | 9450  | 0.5518          | 0.7783   |
| 0.5959        | 43.0  | 9675  | 0.5505          | 0.78     |
| 0.6402        | 44.0  | 9900  | 0.5495          | 0.78     |
| 0.5702        | 45.0  | 10125 | 0.5486          | 0.7817   |
| 0.5765        | 46.0  | 10350 | 0.5481          | 0.7833   |
| 0.6208        | 47.0  | 10575 | 0.5477          | 0.7833   |
| 0.5613        | 48.0  | 10800 | 0.5473          | 0.7833   |
| 0.6326        | 49.0  | 11025 | 0.5471          | 0.7833   |
| 0.5777        | 50.0  | 11250 | 0.5470          | 0.7833   |


### 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