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# smids_5x_beit_base_rms_00001_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: 0.9185
- Accuracy: 0.9133

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2283        | 1.0   | 375   | 0.2339          | 0.9183   |
| 0.0993        | 2.0   | 750   | 0.2718          | 0.9117   |
| 0.0737        | 3.0   | 1125  | 0.3409          | 0.9133   |
| 0.0242        | 4.0   | 1500  | 0.3831          | 0.92     |
| 0.0583        | 5.0   | 1875  | 0.4502          | 0.915    |
| 0.0327        | 6.0   | 2250  | 0.4867          | 0.9183   |
| 0.0228        | 7.0   | 2625  | 0.5343          | 0.9333   |
| 0.0011        | 8.0   | 3000  | 0.6969          | 0.91     |
| 0.0064        | 9.0   | 3375  | 0.8807          | 0.89     |
| 0.0562        | 10.0  | 3750  | 0.8333          | 0.9117   |
| 0.0104        | 11.0  | 4125  | 0.6930          | 0.9083   |
| 0.0118        | 12.0  | 4500  | 0.8317          | 0.8933   |
| 0.0001        | 13.0  | 4875  | 0.8634          | 0.9      |
| 0.0154        | 14.0  | 5250  | 0.7424          | 0.9233   |
| 0.0231        | 15.0  | 5625  | 0.8048          | 0.9133   |
| 0.0           | 16.0  | 6000  | 0.8245          | 0.9083   |
| 0.0137        | 17.0  | 6375  | 0.7565          | 0.92     |
| 0.0069        | 18.0  | 6750  | 0.7751          | 0.9183   |
| 0.0115        | 19.0  | 7125  | 0.7824          | 0.9233   |
| 0.0051        | 20.0  | 7500  | 0.7691          | 0.9183   |
| 0.0           | 21.0  | 7875  | 0.8067          | 0.9117   |
| 0.0207        | 22.0  | 8250  | 0.7817          | 0.915    |
| 0.0053        | 23.0  | 8625  | 0.8276          | 0.9083   |
| 0.0001        | 24.0  | 9000  | 0.7978          | 0.9117   |
| 0.0169        | 25.0  | 9375  | 0.8806          | 0.9067   |
| 0.0007        | 26.0  | 9750  | 0.8278          | 0.9267   |
| 0.0038        | 27.0  | 10125 | 0.9428          | 0.9183   |
| 0.0           | 28.0  | 10500 | 0.8806          | 0.9167   |
| 0.0           | 29.0  | 10875 | 0.8180          | 0.91     |
| 0.0029        | 30.0  | 11250 | 0.9090          | 0.9117   |
| 0.0           | 31.0  | 11625 | 0.8537          | 0.9133   |
| 0.0002        | 32.0  | 12000 | 0.8596          | 0.915    |
| 0.0003        | 33.0  | 12375 | 0.8995          | 0.9183   |
| 0.0           | 34.0  | 12750 | 0.8853          | 0.925    |
| 0.0           | 35.0  | 13125 | 0.8638          | 0.9133   |
| 0.0           | 36.0  | 13500 | 0.8296          | 0.9167   |
| 0.0           | 37.0  | 13875 | 0.8113          | 0.9217   |
| 0.0           | 38.0  | 14250 | 0.8781          | 0.92     |
| 0.027         | 39.0  | 14625 | 0.8890          | 0.92     |
| 0.0038        | 40.0  | 15000 | 0.8330          | 0.925    |
| 0.0           | 41.0  | 15375 | 0.9306          | 0.9167   |
| 0.0           | 42.0  | 15750 | 0.8569          | 0.9183   |
| 0.0           | 43.0  | 16125 | 0.9060          | 0.9133   |
| 0.0           | 44.0  | 16500 | 0.8854          | 0.9167   |
| 0.0           | 45.0  | 16875 | 0.9021          | 0.91     |
| 0.0001        | 46.0  | 17250 | 0.9154          | 0.9133   |
| 0.0           | 47.0  | 17625 | 0.8802          | 0.915    |
| 0.0           | 48.0  | 18000 | 0.8999          | 0.915    |
| 0.0           | 49.0  | 18375 | 0.9100          | 0.9117   |
| 0.0           | 50.0  | 18750 | 0.9185          | 0.9133   |


### 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