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# smids_5x_beit_base_sgd_0001_fold3

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: 0.4604
- Accuracy: 0.8283

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1529        | 1.0   | 375   | 1.2380          | 0.355    |
| 1.0823        | 2.0   | 750   | 1.1537          | 0.38     |
| 0.966         | 3.0   | 1125  | 1.0702          | 0.43     |
| 0.8969        | 4.0   | 1500  | 0.9930          | 0.4833   |
| 0.8438        | 5.0   | 1875  | 0.9234          | 0.5467   |
| 0.8108        | 6.0   | 2250  | 0.8618          | 0.6067   |
| 0.781         | 7.0   | 2625  | 0.8085          | 0.6533   |
| 0.7359        | 8.0   | 3000  | 0.7640          | 0.695    |
| 0.6561        | 9.0   | 3375  | 0.7269          | 0.7067   |
| 0.687         | 10.0  | 3750  | 0.6956          | 0.72     |
| 0.6327        | 11.0  | 4125  | 0.6692          | 0.7333   |
| 0.6325        | 12.0  | 4500  | 0.6472          | 0.7433   |
| 0.6338        | 13.0  | 4875  | 0.6288          | 0.7567   |
| 0.6265        | 14.0  | 5250  | 0.6103          | 0.76     |
| 0.5767        | 15.0  | 5625  | 0.5960          | 0.7667   |
| 0.5529        | 16.0  | 6000  | 0.5830          | 0.7717   |
| 0.6057        | 17.0  | 6375  | 0.5711          | 0.7817   |
| 0.572         | 18.0  | 6750  | 0.5610          | 0.7817   |
| 0.5708        | 19.0  | 7125  | 0.5513          | 0.7833   |
| 0.5256        | 20.0  | 7500  | 0.5427          | 0.7867   |
| 0.4697        | 21.0  | 7875  | 0.5351          | 0.795    |
| 0.5346        | 22.0  | 8250  | 0.5286          | 0.7983   |
| 0.5543        | 23.0  | 8625  | 0.5218          | 0.8017   |
| 0.5387        | 24.0  | 9000  | 0.5157          | 0.8033   |
| 0.5031        | 25.0  | 9375  | 0.5106          | 0.8017   |
| 0.5567        | 26.0  | 9750  | 0.5058          | 0.805    |
| 0.5071        | 27.0  | 10125 | 0.5014          | 0.805    |
| 0.5467        | 28.0  | 10500 | 0.4973          | 0.8083   |
| 0.4516        | 29.0  | 10875 | 0.4933          | 0.8083   |
| 0.497         | 30.0  | 11250 | 0.4901          | 0.8133   |
| 0.5249        | 31.0  | 11625 | 0.4866          | 0.8133   |
| 0.5174        | 32.0  | 12000 | 0.4834          | 0.8167   |
| 0.5605        | 33.0  | 12375 | 0.4805          | 0.8183   |
| 0.5131        | 34.0  | 12750 | 0.4779          | 0.82     |
| 0.4459        | 35.0  | 13125 | 0.4756          | 0.8217   |
| 0.4556        | 36.0  | 13500 | 0.4735          | 0.8217   |
| 0.4756        | 37.0  | 13875 | 0.4716          | 0.8233   |
| 0.4606        | 38.0  | 14250 | 0.4696          | 0.8233   |
| 0.5275        | 39.0  | 14625 | 0.4682          | 0.825    |
| 0.4638        | 40.0  | 15000 | 0.4668          | 0.8233   |
| 0.4141        | 41.0  | 15375 | 0.4657          | 0.8233   |
| 0.4983        | 42.0  | 15750 | 0.4646          | 0.8283   |
| 0.5146        | 43.0  | 16125 | 0.4634          | 0.8267   |
| 0.4784        | 44.0  | 16500 | 0.4625          | 0.8267   |
| 0.4516        | 45.0  | 16875 | 0.4618          | 0.8283   |
| 0.496         | 46.0  | 17250 | 0.4612          | 0.8283   |
| 0.5144        | 47.0  | 17625 | 0.4609          | 0.8283   |
| 0.501         | 48.0  | 18000 | 0.4606          | 0.8283   |
| 0.4693        | 49.0  | 18375 | 0.4604          | 0.8283   |
| 0.4848        | 50.0  | 18750 | 0.4604          | 0.8283   |


### 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