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# smids_3x_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: 0.5470
- Accuracy: 0.7887

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1643        | 1.0   | 225   | 1.2557          | 0.3494   |
| 1.1336        | 2.0   | 450   | 1.1964          | 0.3727   |
| 1.0702        | 3.0   | 675   | 1.1415          | 0.3960   |
| 1.0744        | 4.0   | 900   | 1.0897          | 0.4226   |
| 0.9272        | 5.0   | 1125  | 1.0392          | 0.4526   |
| 0.9348        | 6.0   | 1350  | 0.9924          | 0.4908   |
| 0.9221        | 7.0   | 1575  | 0.9474          | 0.5374   |
| 0.8806        | 8.0   | 1800  | 0.9069          | 0.5890   |
| 0.8541        | 9.0   | 2025  | 0.8693          | 0.6206   |
| 0.8102        | 10.0  | 2250  | 0.8367          | 0.6439   |
| 0.7893        | 11.0  | 2475  | 0.8072          | 0.6672   |
| 0.7786        | 12.0  | 2700  | 0.7812          | 0.6872   |
| 0.7601        | 13.0  | 2925  | 0.7581          | 0.7038   |
| 0.7654        | 14.0  | 3150  | 0.7376          | 0.7105   |
| 0.7556        | 15.0  | 3375  | 0.7195          | 0.7171   |
| 0.7319        | 16.0  | 3600  | 0.7031          | 0.7321   |
| 0.6868        | 17.0  | 3825  | 0.6881          | 0.7354   |
| 0.7278        | 18.0  | 4050  | 0.6745          | 0.7421   |
| 0.6222        | 19.0  | 4275  | 0.6623          | 0.7454   |
| 0.6905        | 20.0  | 4500  | 0.6515          | 0.7471   |
| 0.6715        | 21.0  | 4725  | 0.6419          | 0.7554   |
| 0.7342        | 22.0  | 4950  | 0.6326          | 0.7554   |
| 0.6844        | 23.0  | 5175  | 0.6245          | 0.7621   |
| 0.6577        | 24.0  | 5400  | 0.6173          | 0.7654   |
| 0.6177        | 25.0  | 5625  | 0.6101          | 0.7687   |
| 0.647         | 26.0  | 5850  | 0.6037          | 0.7671   |
| 0.6355        | 27.0  | 6075  | 0.5976          | 0.7704   |
| 0.6059        | 28.0  | 6300  | 0.5926          | 0.7704   |
| 0.5954        | 29.0  | 6525  | 0.5873          | 0.7770   |
| 0.6256        | 30.0  | 6750  | 0.5829          | 0.7787   |
| 0.6261        | 31.0  | 6975  | 0.5789          | 0.7820   |
| 0.5804        | 32.0  | 7200  | 0.5748          | 0.7820   |
| 0.5936        | 33.0  | 7425  | 0.5711          | 0.7854   |
| 0.5647        | 34.0  | 7650  | 0.5682          | 0.7854   |
| 0.6238        | 35.0  | 7875  | 0.5657          | 0.7854   |
| 0.5976        | 36.0  | 8100  | 0.5630          | 0.7854   |
| 0.5852        | 37.0  | 8325  | 0.5605          | 0.7870   |
| 0.5826        | 38.0  | 8550  | 0.5584          | 0.7854   |
| 0.5619        | 39.0  | 8775  | 0.5564          | 0.7854   |
| 0.5946        | 40.0  | 9000  | 0.5547          | 0.7870   |
| 0.5381        | 41.0  | 9225  | 0.5529          | 0.7870   |
| 0.5966        | 42.0  | 9450  | 0.5514          | 0.7870   |
| 0.588         | 43.0  | 9675  | 0.5504          | 0.7870   |
| 0.5705        | 44.0  | 9900  | 0.5494          | 0.7854   |
| 0.6073        | 45.0  | 10125 | 0.5486          | 0.7870   |
| 0.5915        | 46.0  | 10350 | 0.5480          | 0.7887   |
| 0.5988        | 47.0  | 10575 | 0.5476          | 0.7887   |
| 0.542         | 48.0  | 10800 | 0.5472          | 0.7887   |
| 0.5885        | 49.0  | 11025 | 0.5471          | 0.7887   |
| 0.5585        | 50.0  | 11250 | 0.5470          | 0.7887   |


### 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