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# smids_5x_beit_base_sgd_0001_fold2

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: 0.4724
- Accuracy: 0.8037

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0748        | 1.0   | 375   | 1.2296          | 0.3677   |
| 1.052         | 2.0   | 750   | 1.1460          | 0.3943   |
| 0.9644        | 3.0   | 1125  | 1.0650          | 0.4326   |
| 0.8415        | 4.0   | 1500  | 0.9877          | 0.4942   |
| 0.786         | 5.0   | 1875  | 0.9192          | 0.5557   |
| 0.7841        | 6.0   | 2250  | 0.8580          | 0.6323   |
| 0.7782        | 7.0   | 2625  | 0.8061          | 0.6689   |
| 0.7211        | 8.0   | 3000  | 0.7620          | 0.6889   |
| 0.6883        | 9.0   | 3375  | 0.7245          | 0.7138   |
| 0.6641        | 10.0  | 3750  | 0.6925          | 0.7371   |
| 0.6683        | 11.0  | 4125  | 0.6665          | 0.7404   |
| 0.7093        | 12.0  | 4500  | 0.6445          | 0.7454   |
| 0.5818        | 13.0  | 4875  | 0.6259          | 0.7604   |
| 0.5841        | 14.0  | 5250  | 0.6091          | 0.7604   |
| 0.5811        | 15.0  | 5625  | 0.5946          | 0.7637   |
| 0.5799        | 16.0  | 6000  | 0.5819          | 0.7704   |
| 0.5841        | 17.0  | 6375  | 0.5719          | 0.7720   |
| 0.5531        | 18.0  | 6750  | 0.5621          | 0.7770   |
| 0.5613        | 19.0  | 7125  | 0.5532          | 0.7820   |
| 0.5733        | 20.0  | 7500  | 0.5445          | 0.7837   |
| 0.538         | 21.0  | 7875  | 0.5380          | 0.7887   |
| 0.5305        | 22.0  | 8250  | 0.5326          | 0.7903   |
| 0.5558        | 23.0  | 8625  | 0.5259          | 0.7887   |
| 0.5149        | 24.0  | 9000  | 0.5202          | 0.7903   |
| 0.5317        | 25.0  | 9375  | 0.5157          | 0.7903   |
| 0.5391        | 26.0  | 9750  | 0.5118          | 0.7937   |
| 0.557         | 27.0  | 10125 | 0.5073          | 0.7920   |
| 0.4911        | 28.0  | 10500 | 0.5033          | 0.7970   |
| 0.4985        | 29.0  | 10875 | 0.5001          | 0.7937   |
| 0.5262        | 30.0  | 11250 | 0.4968          | 0.7937   |
| 0.4712        | 31.0  | 11625 | 0.4944          | 0.7970   |
| 0.5163        | 32.0  | 12000 | 0.4912          | 0.7953   |
| 0.4489        | 33.0  | 12375 | 0.4890          | 0.7987   |
| 0.4565        | 34.0  | 12750 | 0.4870          | 0.8003   |
| 0.484         | 35.0  | 13125 | 0.4849          | 0.7987   |
| 0.4879        | 36.0  | 13500 | 0.4832          | 0.8020   |
| 0.48          | 37.0  | 13875 | 0.4815          | 0.8053   |
| 0.4581        | 38.0  | 14250 | 0.4797          | 0.8053   |
| 0.4627        | 39.0  | 14625 | 0.4783          | 0.8070   |
| 0.475         | 40.0  | 15000 | 0.4772          | 0.8053   |
| 0.4851        | 41.0  | 15375 | 0.4762          | 0.8037   |
| 0.4434        | 42.0  | 15750 | 0.4753          | 0.8037   |
| 0.4381        | 43.0  | 16125 | 0.4746          | 0.8037   |
| 0.506         | 44.0  | 16500 | 0.4740          | 0.8037   |
| 0.4383        | 45.0  | 16875 | 0.4734          | 0.8037   |
| 0.4915        | 46.0  | 17250 | 0.4730          | 0.8037   |
| 0.4925        | 47.0  | 17625 | 0.4727          | 0.8037   |
| 0.4423        | 48.0  | 18000 | 0.4725          | 0.8037   |
| 0.4853        | 49.0  | 18375 | 0.4724          | 0.8037   |
| 0.5129        | 50.0  | 18750 | 0.4724          | 0.8037   |


### 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