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# smids_10x_deit_small_sgd_00001_fold1

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9431
- Accuracy: 0.5543

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0703        | 1.0   | 751   | 1.0730          | 0.4341   |
| 1.028         | 2.0   | 1502  | 1.0687          | 0.4341   |
| 1.0258        | 3.0   | 2253  | 1.0640          | 0.4374   |
| 1.0492        | 4.0   | 3004  | 1.0592          | 0.4391   |
| 1.036         | 5.0   | 3755  | 1.0544          | 0.4407   |
| 1.0185        | 6.0   | 4506  | 1.0494          | 0.4457   |
| 1.0044        | 7.0   | 5257  | 1.0445          | 0.4524   |
| 1.0106        | 8.0   | 6008  | 1.0397          | 0.4524   |
| 1.0008        | 9.0   | 6759  | 1.0351          | 0.4558   |
| 0.983         | 10.0  | 7510  | 1.0305          | 0.4624   |
| 0.9888        | 11.0  | 8261  | 1.0261          | 0.4691   |
| 0.98          | 12.0  | 9012  | 1.0217          | 0.4758   |
| 0.9777        | 13.0  | 9763  | 1.0175          | 0.4775   |
| 0.9805        | 14.0  | 10514 | 1.0134          | 0.4825   |
| 0.9554        | 15.0  | 11265 | 1.0095          | 0.4875   |
| 0.9727        | 16.0  | 12016 | 1.0055          | 0.4942   |
| 0.9405        | 17.0  | 12767 | 1.0016          | 0.4992   |
| 0.9669        | 18.0  | 13518 | 0.9980          | 0.5042   |
| 0.9407        | 19.0  | 14269 | 0.9944          | 0.5042   |
| 0.9487        | 20.0  | 15020 | 0.9909          | 0.5075   |
| 0.9336        | 21.0  | 15771 | 0.9876          | 0.5092   |
| 0.9505        | 22.0  | 16522 | 0.9843          | 0.5109   |
| 0.9425        | 23.0  | 17273 | 0.9812          | 0.5125   |
| 0.9422        | 24.0  | 18024 | 0.9782          | 0.5175   |
| 0.9397        | 25.0  | 18775 | 0.9753          | 0.5209   |
| 0.9277        | 26.0  | 19526 | 0.9725          | 0.5225   |
| 0.9248        | 27.0  | 20277 | 0.9699          | 0.5326   |
| 0.915         | 28.0  | 21028 | 0.9674          | 0.5342   |
| 0.9341        | 29.0  | 21779 | 0.9650          | 0.5376   |
| 0.9201        | 30.0  | 22530 | 0.9628          | 0.5392   |
| 0.8994        | 31.0  | 23281 | 0.9606          | 0.5376   |
| 0.9167        | 32.0  | 24032 | 0.9586          | 0.5392   |
| 0.8872        | 33.0  | 24783 | 0.9568          | 0.5426   |
| 0.8983        | 34.0  | 25534 | 0.9550          | 0.5426   |
| 0.8839        | 35.0  | 26285 | 0.9534          | 0.5442   |
| 0.9018        | 36.0  | 27036 | 0.9519          | 0.5476   |
| 0.8955        | 37.0  | 27787 | 0.9506          | 0.5492   |
| 0.8964        | 38.0  | 28538 | 0.9493          | 0.5492   |
| 0.9005        | 39.0  | 29289 | 0.9482          | 0.5492   |
| 0.8988        | 40.0  | 30040 | 0.9472          | 0.5526   |
| 0.8967        | 41.0  | 30791 | 0.9463          | 0.5543   |
| 0.8873        | 42.0  | 31542 | 0.9455          | 0.5543   |
| 0.9048        | 43.0  | 32293 | 0.9449          | 0.5543   |
| 0.8665        | 44.0  | 33044 | 0.9443          | 0.5543   |
| 0.8925        | 45.0  | 33795 | 0.9439          | 0.5543   |
| 0.8934        | 46.0  | 34546 | 0.9435          | 0.5543   |
| 0.8656        | 47.0  | 35297 | 0.9433          | 0.5543   |
| 0.9144        | 48.0  | 36048 | 0.9431          | 0.5543   |
| 0.9081        | 49.0  | 36799 | 0.9431          | 0.5543   |
| 0.8986        | 50.0  | 37550 | 0.9431          | 0.5543   |


### 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