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# smids_10x_beit_large_sgd_00001_fold1

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8308
- Accuracy: 0.6361

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1798        | 1.0   | 751   | 1.2530          | 0.3139   |
| 1.1588        | 2.0   | 1502  | 1.2210          | 0.3272   |
| 1.0286        | 3.0   | 2253  | 1.1929          | 0.3272   |
| 1.0699        | 4.0   | 3004  | 1.1680          | 0.3372   |
| 1.0532        | 5.0   | 3755  | 1.1455          | 0.3539   |
| 1.0168        | 6.0   | 4506  | 1.1249          | 0.3589   |
| 1.0334        | 7.0   | 5257  | 1.1059          | 0.3840   |
| 1.006         | 8.0   | 6008  | 1.0879          | 0.3923   |
| 0.9781        | 9.0   | 6759  | 1.0713          | 0.4073   |
| 0.9206        | 10.0  | 7510  | 1.0557          | 0.4324   |
| 0.9599        | 11.0  | 8261  | 1.0410          | 0.4457   |
| 0.8538        | 12.0  | 9012  | 1.0272          | 0.4591   |
| 0.8992        | 13.0  | 9763  | 1.0143          | 0.4725   |
| 0.9105        | 14.0  | 10514 | 1.0019          | 0.4925   |
| 0.8886        | 15.0  | 11265 | 0.9904          | 0.5058   |
| 0.8635        | 16.0  | 12016 | 0.9792          | 0.5209   |
| 0.9091        | 17.0  | 12767 | 0.9687          | 0.5292   |
| 0.8236        | 18.0  | 13518 | 0.9588          | 0.5342   |
| 0.8559        | 19.0  | 14269 | 0.9493          | 0.5426   |
| 0.7879        | 20.0  | 15020 | 0.9403          | 0.5509   |
| 0.765         | 21.0  | 15771 | 0.9320          | 0.5543   |
| 0.8223        | 22.0  | 16522 | 0.9238          | 0.5593   |
| 0.782         | 23.0  | 17273 | 0.9162          | 0.5659   |
| 0.875         | 24.0  | 18024 | 0.9090          | 0.5726   |
| 0.8022        | 25.0  | 18775 | 0.9023          | 0.5793   |
| 0.8471        | 26.0  | 19526 | 0.8959          | 0.5860   |
| 0.7822        | 27.0  | 20277 | 0.8898          | 0.5977   |
| 0.789         | 28.0  | 21028 | 0.8841          | 0.6010   |
| 0.8149        | 29.0  | 21779 | 0.8788          | 0.6027   |
| 0.7987        | 30.0  | 22530 | 0.8738          | 0.6077   |
| 0.7188        | 31.0  | 23281 | 0.8692          | 0.6160   |
| 0.802         | 32.0  | 24032 | 0.8649          | 0.6194   |
| 0.8114        | 33.0  | 24783 | 0.8608          | 0.6194   |
| 0.7414        | 34.0  | 25534 | 0.8570          | 0.6210   |
| 0.766         | 35.0  | 26285 | 0.8536          | 0.6210   |
| 0.7537        | 36.0  | 27036 | 0.8504          | 0.6260   |
| 0.7794        | 37.0  | 27787 | 0.8475          | 0.6277   |
| 0.7455        | 38.0  | 28538 | 0.8448          | 0.6311   |
| 0.7702        | 39.0  | 29289 | 0.8424          | 0.6311   |
| 0.75          | 40.0  | 30040 | 0.8403          | 0.6311   |
| 0.7442        | 41.0  | 30791 | 0.8384          | 0.6344   |
| 0.6885        | 42.0  | 31542 | 0.8367          | 0.6344   |
| 0.7317        | 43.0  | 32293 | 0.8353          | 0.6344   |
| 0.7377        | 44.0  | 33044 | 0.8340          | 0.6344   |
| 0.7327        | 45.0  | 33795 | 0.8330          | 0.6344   |
| 0.752         | 46.0  | 34546 | 0.8322          | 0.6361   |
| 0.7091        | 47.0  | 35297 | 0.8315          | 0.6361   |
| 0.7684        | 48.0  | 36048 | 0.8311          | 0.6361   |
| 0.7425        | 49.0  | 36799 | 0.8309          | 0.6361   |
| 0.7641        | 50.0  | 37550 | 0.8308          | 0.6361   |


### 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