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# smids_10x_deit_tiny_adamax_001_fold1

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1377
- Accuracy: 0.8948

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.387         | 1.0   | 751   | 0.3856          | 0.8447   |
| 0.2275        | 2.0   | 1502  | 0.3799          | 0.8497   |
| 0.1732        | 3.0   | 2253  | 0.3628          | 0.8898   |
| 0.1418        | 4.0   | 3004  | 0.3720          | 0.8848   |
| 0.1851        | 5.0   | 3755  | 0.4163          | 0.8497   |
| 0.1168        | 6.0   | 4506  | 0.4228          | 0.8915   |
| 0.1217        | 7.0   | 5257  | 0.4050          | 0.8965   |
| 0.0972        | 8.0   | 6008  | 0.4659          | 0.8881   |
| 0.0717        | 9.0   | 6759  | 0.4692          | 0.8848   |
| 0.0615        | 10.0  | 7510  | 0.5939          | 0.8748   |
| 0.0582        | 11.0  | 8261  | 0.5202          | 0.8898   |
| 0.0569        | 12.0  | 9012  | 0.5681          | 0.8982   |
| 0.0142        | 13.0  | 9763  | 0.7223          | 0.8815   |
| 0.0849        | 14.0  | 10514 | 0.6292          | 0.8948   |
| 0.0289        | 15.0  | 11265 | 0.7113          | 0.8898   |
| 0.0438        | 16.0  | 12016 | 0.6702          | 0.8982   |
| 0.0561        | 17.0  | 12767 | 0.7629          | 0.8765   |
| 0.0013        | 18.0  | 13518 | 0.7639          | 0.8865   |
| 0.0173        | 19.0  | 14269 | 0.6756          | 0.8965   |
| 0.0044        | 20.0  | 15020 | 0.7365          | 0.8965   |
| 0.013         | 21.0  | 15771 | 0.8044          | 0.8831   |
| 0.0056        | 22.0  | 16522 | 0.7938          | 0.8915   |
| 0.0006        | 23.0  | 17273 | 0.8954          | 0.8848   |
| 0.0157        | 24.0  | 18024 | 0.8083          | 0.8998   |
| 0.0002        | 25.0  | 18775 | 0.8156          | 0.8965   |
| 0.0001        | 26.0  | 19526 | 0.8204          | 0.8982   |
| 0.0087        | 27.0  | 20277 | 0.8556          | 0.8948   |
| 0.0001        | 28.0  | 21028 | 0.8189          | 0.9048   |
| 0.0132        | 29.0  | 21779 | 0.8401          | 0.9065   |
| 0.0001        | 30.0  | 22530 | 0.9274          | 0.8915   |
| 0.0           | 31.0  | 23281 | 0.9668          | 0.8965   |
| 0.0153        | 32.0  | 24032 | 0.9746          | 0.8932   |
| 0.0           | 33.0  | 24783 | 1.0269          | 0.8881   |
| 0.0           | 34.0  | 25534 | 1.0125          | 0.8948   |
| 0.0           | 35.0  | 26285 | 1.0419          | 0.8898   |
| 0.0003        | 36.0  | 27036 | 1.0764          | 0.8898   |
| 0.0           | 37.0  | 27787 | 1.0824          | 0.8915   |
| 0.0           | 38.0  | 28538 | 1.0882          | 0.8898   |
| 0.0           | 39.0  | 29289 | 1.0563          | 0.8932   |
| 0.0           | 40.0  | 30040 | 1.0771          | 0.8915   |
| 0.0           | 41.0  | 30791 | 1.0705          | 0.8948   |
| 0.0           | 42.0  | 31542 | 1.0752          | 0.8932   |
| 0.0           | 43.0  | 32293 | 1.1011          | 0.8948   |
| 0.0           | 44.0  | 33044 | 1.1049          | 0.8948   |
| 0.0           | 45.0  | 33795 | 1.1132          | 0.8948   |
| 0.0           | 46.0  | 34546 | 1.1208          | 0.8965   |
| 0.0           | 47.0  | 35297 | 1.1280          | 0.8948   |
| 0.0           | 48.0  | 36048 | 1.1328          | 0.8948   |
| 0.0           | 49.0  | 36799 | 1.1361          | 0.8948   |
| 0.0           | 50.0  | 37550 | 1.1377          | 0.8948   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm