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# smids_3x_beit_base_sgd_0001_fold4

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: 0.5543
- Accuracy: 0.77

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2016        | 1.0   | 225   | 1.2841          | 0.345    |
| 1.1719        | 2.0   | 450   | 1.2211          | 0.3617   |
| 1.0758        | 3.0   | 675   | 1.1630          | 0.3733   |
| 1.0147        | 4.0   | 900   | 1.1086          | 0.4033   |
| 1.0074        | 5.0   | 1125  | 1.0560          | 0.4317   |
| 0.9405        | 6.0   | 1350  | 1.0063          | 0.4617   |
| 0.9199        | 7.0   | 1575  | 0.9602          | 0.51     |
| 0.9125        | 8.0   | 1800  | 0.9177          | 0.5617   |
| 0.8654        | 9.0   | 2025  | 0.8771          | 0.6017   |
| 0.8229        | 10.0  | 2250  | 0.8432          | 0.6333   |
| 0.8209        | 11.0  | 2475  | 0.8129          | 0.6567   |
| 0.775         | 12.0  | 2700  | 0.7860          | 0.675    |
| 0.7435        | 13.0  | 2925  | 0.7620          | 0.6883   |
| 0.7034        | 14.0  | 3150  | 0.7408          | 0.695    |
| 0.7434        | 15.0  | 3375  | 0.7223          | 0.7033   |
| 0.7412        | 16.0  | 3600  | 0.7055          | 0.7133   |
| 0.6871        | 17.0  | 3825  | 0.6906          | 0.7167   |
| 0.6997        | 18.0  | 4050  | 0.6769          | 0.725    |
| 0.6998        | 19.0  | 4275  | 0.6646          | 0.7267   |
| 0.6623        | 20.0  | 4500  | 0.6540          | 0.7283   |
| 0.668         | 21.0  | 4725  | 0.6441          | 0.73     |
| 0.6697        | 22.0  | 4950  | 0.6349          | 0.7317   |
| 0.6394        | 23.0  | 5175  | 0.6268          | 0.7383   |
| 0.6267        | 24.0  | 5400  | 0.6193          | 0.7383   |
| 0.6154        | 25.0  | 5625  | 0.6125          | 0.7433   |
| 0.5813        | 26.0  | 5850  | 0.6070          | 0.745    |
| 0.612         | 27.0  | 6075  | 0.6014          | 0.7483   |
| 0.6011        | 28.0  | 6300  | 0.5964          | 0.7483   |
| 0.5913        | 29.0  | 6525  | 0.5915          | 0.7517   |
| 0.5609        | 30.0  | 6750  | 0.5872          | 0.76     |
| 0.5861        | 31.0  | 6975  | 0.5835          | 0.7617   |
| 0.5483        | 32.0  | 7200  | 0.5800          | 0.76     |
| 0.5986        | 33.0  | 7425  | 0.5766          | 0.7633   |
| 0.619         | 34.0  | 7650  | 0.5736          | 0.7617   |
| 0.5813        | 35.0  | 7875  | 0.5710          | 0.765    |
| 0.6084        | 36.0  | 8100  | 0.5683          | 0.7667   |
| 0.6052        | 37.0  | 8325  | 0.5664          | 0.765    |
| 0.5601        | 38.0  | 8550  | 0.5646          | 0.765    |
| 0.5878        | 39.0  | 8775  | 0.5631          | 0.7633   |
| 0.6072        | 40.0  | 9000  | 0.5616          | 0.7633   |
| 0.5597        | 41.0  | 9225  | 0.5601          | 0.7683   |
| 0.5694        | 42.0  | 9450  | 0.5588          | 0.7667   |
| 0.5553        | 43.0  | 9675  | 0.5575          | 0.77     |
| 0.5942        | 44.0  | 9900  | 0.5566          | 0.77     |
| 0.6005        | 45.0  | 10125 | 0.5559          | 0.77     |
| 0.58          | 46.0  | 10350 | 0.5553          | 0.77     |
| 0.5814        | 47.0  | 10575 | 0.5548          | 0.77     |
| 0.5609        | 48.0  | 10800 | 0.5545          | 0.7717   |
| 0.6076        | 49.0  | 11025 | 0.5543          | 0.77     |
| 0.5819        | 50.0  | 11250 | 0.5543          | 0.77     |


### 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