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# smids_10x_deit_tiny_rms_0001_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: 0.5641
- Accuracy: 0.7746

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9582        | 1.0   | 751   | 0.8489          | 0.5409   |
| 0.6559        | 2.0   | 1502  | 0.7002          | 0.6861   |
| 0.7114        | 3.0   | 2253  | 0.6777          | 0.6761   |
| 0.6817        | 4.0   | 3004  | 0.6465          | 0.6928   |
| 0.7123        | 5.0   | 3755  | 0.7746          | 0.6277   |
| 0.7345        | 6.0   | 4506  | 0.6493          | 0.7062   |
| 0.5686        | 7.0   | 5257  | 0.6822          | 0.7145   |
| 0.6113        | 8.0   | 6008  | 0.7258          | 0.6594   |
| 0.5841        | 9.0   | 6759  | 0.7856          | 0.6694   |
| 0.5988        | 10.0  | 7510  | 0.5594          | 0.7746   |
| 0.5433        | 11.0  | 8261  | 0.5624          | 0.7696   |
| 0.5465        | 12.0  | 9012  | 0.6273          | 0.7396   |
| 0.5648        | 13.0  | 9763  | 0.5471          | 0.7646   |
| 0.5274        | 14.0  | 10514 | 0.5762          | 0.7596   |
| 0.5506        | 15.0  | 11265 | 0.6408          | 0.7279   |
| 0.5782        | 16.0  | 12016 | 0.6203          | 0.7346   |
| 0.6505        | 17.0  | 12767 | 0.6410          | 0.7429   |
| 0.5965        | 18.0  | 13518 | 0.6045          | 0.7245   |
| 0.5759        | 19.0  | 14269 | 0.6441          | 0.7229   |
| 0.6397        | 20.0  | 15020 | 0.5941          | 0.7529   |
| 0.5964        | 21.0  | 15771 | 0.5925          | 0.7496   |
| 0.5874        | 22.0  | 16522 | 0.5857          | 0.7462   |
| 0.4809        | 23.0  | 17273 | 0.5684          | 0.7596   |
| 0.5688        | 24.0  | 18024 | 0.5817          | 0.7663   |
| 0.5465        | 25.0  | 18775 | 0.5474          | 0.7663   |
| 0.5665        | 26.0  | 19526 | 0.7830          | 0.6611   |
| 0.5896        | 27.0  | 20277 | 0.5680          | 0.7513   |
| 0.6236        | 28.0  | 21028 | 0.6568          | 0.6811   |
| 0.5166        | 29.0  | 21779 | 0.6086          | 0.7446   |
| 0.452         | 30.0  | 22530 | 0.5634          | 0.7579   |
| 0.4919        | 31.0  | 23281 | 0.5616          | 0.7780   |
| 0.5586        | 32.0  | 24032 | 0.5217          | 0.7679   |
| 0.4558        | 33.0  | 24783 | 0.5662          | 0.7513   |
| 0.4855        | 34.0  | 25534 | 0.5721          | 0.7613   |
| 0.4522        | 35.0  | 26285 | 0.5872          | 0.7663   |
| 0.527         | 36.0  | 27036 | 0.5260          | 0.7696   |
| 0.5143        | 37.0  | 27787 | 0.5855          | 0.7429   |
| 0.4104        | 38.0  | 28538 | 0.5552          | 0.7713   |
| 0.3918        | 39.0  | 29289 | 0.5587          | 0.7763   |
| 0.4719        | 40.0  | 30040 | 0.5354          | 0.7863   |
| 0.4591        | 41.0  | 30791 | 0.5453          | 0.7629   |
| 0.3895        | 42.0  | 31542 | 0.5592          | 0.7746   |
| 0.4622        | 43.0  | 32293 | 0.5533          | 0.7796   |
| 0.3949        | 44.0  | 33044 | 0.5492          | 0.7947   |
| 0.4199        | 45.0  | 33795 | 0.5523          | 0.7780   |
| 0.4316        | 46.0  | 34546 | 0.5332          | 0.7896   |
| 0.3892        | 47.0  | 35297 | 0.5380          | 0.7846   |
| 0.3721        | 48.0  | 36048 | 0.5481          | 0.7880   |
| 0.3964        | 49.0  | 36799 | 0.5559          | 0.7846   |
| 0.3446        | 50.0  | 37550 | 0.5641          | 0.7746   |


### 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