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# smids_3x_beit_base_rms_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: 1.8372
- Accuracy: 0.81

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.787         | 1.0   | 225   | 0.7726          | 0.5917   |
| 0.7085        | 2.0   | 450   | 0.7059          | 0.6467   |
| 0.6145        | 3.0   | 675   | 0.6591          | 0.6983   |
| 0.6005        | 4.0   | 900   | 0.5422          | 0.7817   |
| 0.572         | 5.0   | 1125  | 0.5970          | 0.7567   |
| 0.4372        | 6.0   | 1350  | 0.5610          | 0.785    |
| 0.3918        | 7.0   | 1575  | 0.5957          | 0.7917   |
| 0.4058        | 8.0   | 1800  | 0.5296          | 0.7933   |
| 0.3971        | 9.0   | 2025  | 0.6041          | 0.7833   |
| 0.3274        | 10.0  | 2250  | 0.5347          | 0.8      |
| 0.2417        | 11.0  | 2475  | 0.6768          | 0.785    |
| 0.1989        | 12.0  | 2700  | 0.6501          | 0.8133   |
| 0.2222        | 13.0  | 2925  | 0.6337          | 0.7933   |
| 0.1654        | 14.0  | 3150  | 0.7865          | 0.7867   |
| 0.1241        | 15.0  | 3375  | 0.7840          | 0.8033   |
| 0.1208        | 16.0  | 3600  | 0.9856          | 0.795    |
| 0.0877        | 17.0  | 3825  | 1.0442          | 0.7767   |
| 0.1165        | 18.0  | 4050  | 0.9465          | 0.8117   |
| 0.1328        | 19.0  | 4275  | 0.8299          | 0.81     |
| 0.0427        | 20.0  | 4500  | 1.1880          | 0.7917   |
| 0.0826        | 21.0  | 4725  | 1.0665          | 0.8083   |
| 0.0679        | 22.0  | 4950  | 1.2201          | 0.7917   |
| 0.1018        | 23.0  | 5175  | 1.1824          | 0.8      |
| 0.0255        | 24.0  | 5400  | 1.2359          | 0.8117   |
| 0.0956        | 25.0  | 5625  | 1.2156          | 0.805    |
| 0.0725        | 26.0  | 5850  | 1.3671          | 0.81     |
| 0.0849        | 27.0  | 6075  | 1.3399          | 0.7917   |
| 0.068         | 28.0  | 6300  | 1.3279          | 0.8117   |
| 0.0512        | 29.0  | 6525  | 1.1460          | 0.82     |
| 0.0439        | 30.0  | 6750  | 1.4730          | 0.8017   |
| 0.0414        | 31.0  | 6975  | 1.2224          | 0.8067   |
| 0.0174        | 32.0  | 7200  | 1.6967          | 0.7983   |
| 0.0407        | 33.0  | 7425  | 1.5401          | 0.7983   |
| 0.0316        | 34.0  | 7650  | 1.2844          | 0.8017   |
| 0.0008        | 35.0  | 7875  | 1.7477          | 0.805    |
| 0.0104        | 36.0  | 8100  | 1.5173          | 0.8167   |
| 0.0005        | 37.0  | 8325  | 1.6340          | 0.7967   |
| 0.0286        | 38.0  | 8550  | 1.4323          | 0.7983   |
| 0.0292        | 39.0  | 8775  | 1.4953          | 0.805    |
| 0.0108        | 40.0  | 9000  | 1.6930          | 0.8183   |
| 0.022         | 41.0  | 9225  | 1.7083          | 0.8033   |
| 0.0101        | 42.0  | 9450  | 1.8030          | 0.8083   |
| 0.0122        | 43.0  | 9675  | 1.8925          | 0.8133   |
| 0.0071        | 44.0  | 9900  | 1.7250          | 0.815    |
| 0.0004        | 45.0  | 10125 | 1.7937          | 0.8017   |
| 0.0008        | 46.0  | 10350 | 1.9056          | 0.8067   |
| 0.0003        | 47.0  | 10575 | 1.8311          | 0.8083   |
| 0.0001        | 48.0  | 10800 | 1.9401          | 0.8033   |
| 0.0001        | 49.0  | 11025 | 1.8499          | 0.8083   |
| 0.0           | 50.0  | 11250 | 1.8372          | 0.81     |


### 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