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# smids_5x_beit_base_rms_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: 1.3488
- Accuracy: 0.855

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5467        | 1.0   | 375   | 0.5969          | 0.7483   |
| 0.4112        | 2.0   | 750   | 0.6522          | 0.7967   |
| 0.3586        | 3.0   | 1125  | 0.4558          | 0.83     |
| 0.3318        | 4.0   | 1500  | 0.3669          | 0.8567   |
| 0.318         | 5.0   | 1875  | 0.4227          | 0.8267   |
| 0.2611        | 6.0   | 2250  | 0.4142          | 0.8467   |
| 0.2866        | 7.0   | 2625  | 0.4534          | 0.83     |
| 0.2297        | 8.0   | 3000  | 0.4296          | 0.8517   |
| 0.1623        | 9.0   | 3375  | 0.5359          | 0.835    |
| 0.1313        | 10.0  | 3750  | 0.5677          | 0.8433   |
| 0.1856        | 11.0  | 4125  | 0.5198          | 0.8667   |
| 0.087         | 12.0  | 4500  | 0.6463          | 0.8417   |
| 0.0974        | 13.0  | 4875  | 0.5874          | 0.8417   |
| 0.0478        | 14.0  | 5250  | 0.7058          | 0.84     |
| 0.0326        | 15.0  | 5625  | 0.7427          | 0.8283   |
| 0.0198        | 16.0  | 6000  | 0.8945          | 0.84     |
| 0.0746        | 17.0  | 6375  | 0.8489          | 0.8333   |
| 0.1024        | 18.0  | 6750  | 0.7564          | 0.8383   |
| 0.0499        | 19.0  | 7125  | 0.8028          | 0.8483   |
| 0.0808        | 20.0  | 7500  | 1.0400          | 0.8267   |
| 0.0495        | 21.0  | 7875  | 1.0596          | 0.83     |
| 0.0441        | 22.0  | 8250  | 0.9512          | 0.85     |
| 0.0385        | 23.0  | 8625  | 0.8380          | 0.8483   |
| 0.0162        | 24.0  | 9000  | 1.0671          | 0.8517   |
| 0.0061        | 25.0  | 9375  | 0.8747          | 0.86     |
| 0.0284        | 26.0  | 9750  | 1.0398          | 0.815    |
| 0.0446        | 27.0  | 10125 | 0.9748          | 0.845    |
| 0.0208        | 28.0  | 10500 | 1.0700          | 0.8517   |
| 0.0357        | 29.0  | 10875 | 1.0579          | 0.845    |
| 0.0301        | 30.0  | 11250 | 0.9043          | 0.8583   |
| 0.0099        | 31.0  | 11625 | 0.9420          | 0.8533   |
| 0.0327        | 32.0  | 12000 | 1.0192          | 0.8467   |
| 0.0502        | 33.0  | 12375 | 0.8952          | 0.8517   |
| 0.0352        | 34.0  | 12750 | 0.9041          | 0.8667   |
| 0.0188        | 35.0  | 13125 | 1.2059          | 0.8433   |
| 0.0229        | 36.0  | 13500 | 1.2761          | 0.84     |
| 0.0123        | 37.0  | 13875 | 1.1077          | 0.8583   |
| 0.0002        | 38.0  | 14250 | 1.1468          | 0.85     |
| 0.0009        | 39.0  | 14625 | 1.1590          | 0.8617   |
| 0.0211        | 40.0  | 15000 | 1.3901          | 0.8683   |
| 0.001         | 41.0  | 15375 | 1.2933          | 0.8533   |
| 0.0077        | 42.0  | 15750 | 1.1576          | 0.8583   |
| 0.0369        | 43.0  | 16125 | 1.3070          | 0.8433   |
| 0.0132        | 44.0  | 16500 | 1.0120          | 0.8633   |
| 0.0003        | 45.0  | 16875 | 1.2641          | 0.8633   |
| 0.0001        | 46.0  | 17250 | 1.2268          | 0.8633   |
| 0.0004        | 47.0  | 17625 | 1.1854          | 0.8583   |
| 0.0001        | 48.0  | 18000 | 1.3326          | 0.865    |
| 0.0187        | 49.0  | 18375 | 1.3505          | 0.8567   |
| 0.0011        | 50.0  | 18750 | 1.3488          | 0.855    |


### 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