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# smids_5x_beit_base_sgd_00001_fold1

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: 1.1524
- Accuracy: 0.3706

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2623        | 1.0   | 376   | 1.3473          | 0.3088   |
| 1.2409        | 2.0   | 752   | 1.3383          | 0.3122   |
| 1.2444        | 3.0   | 1128  | 1.3298          | 0.3105   |
| 1.1982        | 4.0   | 1504  | 1.3215          | 0.3105   |
| 1.2082        | 5.0   | 1880  | 1.3136          | 0.3105   |
| 1.1814        | 6.0   | 2256  | 1.3057          | 0.3139   |
| 1.1633        | 7.0   | 2632  | 1.2980          | 0.3172   |
| 1.2064        | 8.0   | 3008  | 1.2906          | 0.3205   |
| 1.1298        | 9.0   | 3384  | 1.2834          | 0.3205   |
| 1.1231        | 10.0  | 3760  | 1.2765          | 0.3322   |
| 1.171         | 11.0  | 4136  | 1.2696          | 0.3372   |
| 1.1505        | 12.0  | 4512  | 1.2632          | 0.3439   |
| 1.1137        | 13.0  | 4888  | 1.2569          | 0.3472   |
| 1.1229        | 14.0  | 5264  | 1.2508          | 0.3456   |
| 1.1641        | 15.0  | 5640  | 1.2450          | 0.3439   |
| 1.1335        | 16.0  | 6016  | 1.2391          | 0.3439   |
| 1.0584        | 17.0  | 6392  | 1.2337          | 0.3439   |
| 1.1251        | 18.0  | 6768  | 1.2284          | 0.3456   |
| 1.105         | 19.0  | 7144  | 1.2233          | 0.3506   |
| 1.0972        | 20.0  | 7520  | 1.2186          | 0.3506   |
| 1.0751        | 21.0  | 7896  | 1.2139          | 0.3539   |
| 1.0864        | 22.0  | 8272  | 1.2094          | 0.3539   |
| 1.1021        | 23.0  | 8648  | 1.2051          | 0.3539   |
| 1.1159        | 24.0  | 9024  | 1.2011          | 0.3539   |
| 1.0862        | 25.0  | 9400  | 1.1972          | 0.3556   |
| 1.0706        | 26.0  | 9776  | 1.1934          | 0.3589   |
| 1.0809        | 27.0  | 10152 | 1.1898          | 0.3589   |
| 1.0663        | 28.0  | 10528 | 1.1865          | 0.3589   |
| 1.0982        | 29.0  | 10904 | 1.1833          | 0.3606   |
| 1.1121        | 30.0  | 11280 | 1.1802          | 0.3623   |
| 1.0485        | 31.0  | 11656 | 1.1773          | 0.3656   |
| 1.0472        | 32.0  | 12032 | 1.1746          | 0.3656   |
| 1.0601        | 33.0  | 12408 | 1.1721          | 0.3689   |
| 1.0381        | 34.0  | 12784 | 1.1697          | 0.3689   |
| 1.072         | 35.0  | 13160 | 1.1675          | 0.3689   |
| 1.087         | 36.0  | 13536 | 1.1654          | 0.3689   |
| 1.0074        | 37.0  | 13912 | 1.1635          | 0.3706   |
| 1.0562        | 38.0  | 14288 | 1.1618          | 0.3689   |
| 1.0371        | 39.0  | 14664 | 1.1602          | 0.3689   |
| 1.0517        | 40.0  | 15040 | 1.1588          | 0.3706   |
| 1.0384        | 41.0  | 15416 | 1.1575          | 0.3706   |
| 1.0222        | 42.0  | 15792 | 1.1563          | 0.3706   |
| 1.0143        | 43.0  | 16168 | 1.1553          | 0.3706   |
| 0.9973        | 44.0  | 16544 | 1.1545          | 0.3689   |
| 1.0445        | 45.0  | 16920 | 1.1538          | 0.3689   |
| 1.0408        | 46.0  | 17296 | 1.1532          | 0.3706   |
| 1.0166        | 47.0  | 17672 | 1.1528          | 0.3706   |
| 1.0266        | 48.0  | 18048 | 1.1525          | 0.3706   |
| 1.0337        | 49.0  | 18424 | 1.1524          | 0.3706   |
| 1.0214        | 50.0  | 18800 | 1.1524          | 0.3706   |


### 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