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# smids_5x_beit_base_rms_001_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: 1.2450
- Accuracy: 0.7883

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8383        | 1.0   | 375   | 0.9251          | 0.4967   |
| 0.7811        | 2.0   | 750   | 0.8274          | 0.55     |
| 0.7757        | 3.0   | 1125  | 0.8322          | 0.55     |
| 0.774         | 4.0   | 1500  | 0.7903          | 0.5667   |
| 0.7988        | 5.0   | 1875  | 0.7818          | 0.59     |
| 0.7926        | 6.0   | 2250  | 0.7711          | 0.595    |
| 0.7549        | 7.0   | 2625  | 0.7682          | 0.6267   |
| 0.7997        | 8.0   | 3000  | 0.7569          | 0.61     |
| 0.6926        | 9.0   | 3375  | 0.7561          | 0.6417   |
| 0.7413        | 10.0  | 3750  | 0.7251          | 0.6567   |
| 0.6722        | 11.0  | 4125  | 0.7285          | 0.6533   |
| 0.7582        | 12.0  | 4500  | 0.7029          | 0.66     |
| 0.6728        | 13.0  | 4875  | 0.7283          | 0.6433   |
| 0.6373        | 14.0  | 5250  | 0.7252          | 0.6333   |
| 0.648         | 15.0  | 5625  | 0.7000          | 0.67     |
| 0.6675        | 16.0  | 6000  | 0.7072          | 0.6683   |
| 0.7316        | 17.0  | 6375  | 0.7063          | 0.6717   |
| 0.7151        | 18.0  | 6750  | 0.6856          | 0.6683   |
| 0.6082        | 19.0  | 7125  | 0.6800          | 0.6817   |
| 0.6879        | 20.0  | 7500  | 0.6816          | 0.6733   |
| 0.5586        | 21.0  | 7875  | 0.6735          | 0.695    |
| 0.6065        | 22.0  | 8250  | 0.6507          | 0.71     |
| 0.5783        | 23.0  | 8625  | 0.6597          | 0.69     |
| 0.6456        | 24.0  | 9000  | 0.6102          | 0.74     |
| 0.5238        | 25.0  | 9375  | 0.6683          | 0.7117   |
| 0.5326        | 26.0  | 9750  | 0.6240          | 0.7183   |
| 0.5499        | 27.0  | 10125 | 0.6403          | 0.7083   |
| 0.5607        | 28.0  | 10500 | 0.5945          | 0.7417   |
| 0.4887        | 29.0  | 10875 | 0.6536          | 0.71     |
| 0.5354        | 30.0  | 11250 | 0.5785          | 0.725    |
| 0.5136        | 31.0  | 11625 | 0.6072          | 0.7517   |
| 0.5448        | 32.0  | 12000 | 0.6265          | 0.7383   |
| 0.4542        | 33.0  | 12375 | 0.6265          | 0.7417   |
| 0.4208        | 34.0  | 12750 | 0.6113          | 0.745    |
| 0.3509        | 35.0  | 13125 | 0.6279          | 0.7467   |
| 0.4112        | 36.0  | 13500 | 0.6145          | 0.74     |
| 0.3719        | 37.0  | 13875 | 0.6674          | 0.745    |
| 0.3029        | 38.0  | 14250 | 0.6977          | 0.7583   |
| 0.3416        | 39.0  | 14625 | 0.6751          | 0.7717   |
| 0.3246        | 40.0  | 15000 | 0.6878          | 0.7633   |
| 0.2432        | 41.0  | 15375 | 0.6417          | 0.79     |
| 0.2014        | 42.0  | 15750 | 0.7882          | 0.78     |
| 0.2354        | 43.0  | 16125 | 0.8175          | 0.7817   |
| 0.1797        | 44.0  | 16500 | 0.8553          | 0.79     |
| 0.1419        | 45.0  | 16875 | 0.9481          | 0.765    |
| 0.1815        | 46.0  | 17250 | 1.0306          | 0.765    |
| 0.1604        | 47.0  | 17625 | 1.0263          | 0.765    |
| 0.103         | 48.0  | 18000 | 1.1281          | 0.7833   |
| 0.0441        | 49.0  | 18375 | 1.2055          | 0.79     |
| 0.0741        | 50.0  | 18750 | 1.2450          | 0.7883   |


### 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