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# smids_10x_deit_tiny_rms_00001_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: 0.9553
- Accuracy: 0.9115

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2396        | 1.0   | 751   | 0.2956          | 0.8932   |
| 0.1563        | 2.0   | 1502  | 0.3511          | 0.8748   |
| 0.1378        | 3.0   | 2253  | 0.3209          | 0.9032   |
| 0.0749        | 4.0   | 3004  | 0.4152          | 0.8965   |
| 0.0902        | 5.0   | 3755  | 0.4990          | 0.8998   |
| 0.0311        | 6.0   | 4506  | 0.6972          | 0.8948   |
| 0.0511        | 7.0   | 5257  | 0.6707          | 0.8915   |
| 0.0692        | 8.0   | 6008  | 0.7791          | 0.8948   |
| 0.0655        | 9.0   | 6759  | 0.7801          | 0.8965   |
| 0.0191        | 10.0  | 7510  | 0.8995          | 0.8948   |
| 0.0314        | 11.0  | 8261  | 0.8069          | 0.8965   |
| 0.0092        | 12.0  | 9012  | 0.8789          | 0.8881   |
| 0.0207        | 13.0  | 9763  | 0.9080          | 0.8915   |
| 0.0001        | 14.0  | 10514 | 0.9075          | 0.8982   |
| 0.0137        | 15.0  | 11265 | 1.1216          | 0.8915   |
| 0.0289        | 16.0  | 12016 | 1.0268          | 0.8932   |
| 0.0749        | 17.0  | 12767 | 0.9684          | 0.8965   |
| 0.0004        | 18.0  | 13518 | 0.9374          | 0.8948   |
| 0.0016        | 19.0  | 14269 | 0.9146          | 0.8998   |
| 0.0           | 20.0  | 15020 | 0.8660          | 0.9115   |
| 0.0           | 21.0  | 15771 | 0.8768          | 0.9132   |
| 0.0205        | 22.0  | 16522 | 0.9737          | 0.8932   |
| 0.0           | 23.0  | 17273 | 0.8857          | 0.9065   |
| 0.0           | 24.0  | 18024 | 0.9206          | 0.9015   |
| 0.0           | 25.0  | 18775 | 0.9882          | 0.9032   |
| 0.0051        | 26.0  | 19526 | 0.9311          | 0.9048   |
| 0.0005        | 27.0  | 20277 | 0.9916          | 0.8831   |
| 0.0           | 28.0  | 21028 | 0.8978          | 0.9065   |
| 0.0147        | 29.0  | 21779 | 0.9817          | 0.8998   |
| 0.0           | 30.0  | 22530 | 0.9072          | 0.9132   |
| 0.0           | 31.0  | 23281 | 0.9000          | 0.9032   |
| 0.0           | 32.0  | 24032 | 0.9908          | 0.9048   |
| 0.0           | 33.0  | 24783 | 0.9477          | 0.8998   |
| 0.0001        | 34.0  | 25534 | 0.9361          | 0.8998   |
| 0.0           | 35.0  | 26285 | 0.9285          | 0.9048   |
| 0.0           | 36.0  | 27036 | 0.9622          | 0.9048   |
| 0.0           | 37.0  | 27787 | 0.9080          | 0.9082   |
| 0.0           | 38.0  | 28538 | 1.0318          | 0.9065   |
| 0.0           | 39.0  | 29289 | 0.8954          | 0.9115   |
| 0.0           | 40.0  | 30040 | 0.9047          | 0.9098   |
| 0.0           | 41.0  | 30791 | 0.9568          | 0.9098   |
| 0.0           | 42.0  | 31542 | 0.9648          | 0.9082   |
| 0.0           | 43.0  | 32293 | 0.9575          | 0.9098   |
| 0.0           | 44.0  | 33044 | 0.9498          | 0.9132   |
| 0.0           | 45.0  | 33795 | 0.9583          | 0.9098   |
| 0.0           | 46.0  | 34546 | 0.9523          | 0.9115   |
| 0.0           | 47.0  | 35297 | 0.9525          | 0.9115   |
| 0.0           | 48.0  | 36048 | 0.9545          | 0.9115   |
| 0.0           | 49.0  | 36799 | 0.9543          | 0.9115   |
| 0.0           | 50.0  | 37550 | 0.9553          | 0.9115   |


### 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