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# smids_5x_deit_tiny_sgd_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: 0.5289
- Accuracy: 0.7763

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.163         | 1.0   | 376   | 1.1394          | 0.3756   |
| 1.1206        | 2.0   | 752   | 1.0830          | 0.4240   |
| 1.0283        | 3.0   | 1128  | 1.0398          | 0.4624   |
| 1.0304        | 4.0   | 1504  | 1.0025          | 0.4925   |
| 0.9327        | 5.0   | 1880  | 0.9675          | 0.5159   |
| 0.9025        | 6.0   | 2256  | 0.9343          | 0.5342   |
| 0.813         | 7.0   | 2632  | 0.9040          | 0.5492   |
| 0.8373        | 8.0   | 3008  | 0.8750          | 0.5793   |
| 0.8156        | 9.0   | 3384  | 0.8442          | 0.6093   |
| 0.7741        | 10.0  | 3760  | 0.8156          | 0.6344   |
| 0.6787        | 11.0  | 4136  | 0.7888          | 0.6477   |
| 0.6914        | 12.0  | 4512  | 0.7651          | 0.6561   |
| 0.6673        | 13.0  | 4888  | 0.7424          | 0.6761   |
| 0.6471        | 14.0  | 5264  | 0.7219          | 0.6895   |
| 0.5827        | 15.0  | 5640  | 0.7043          | 0.6978   |
| 0.5679        | 16.0  | 6016  | 0.6891          | 0.7062   |
| 0.5387        | 17.0  | 6392  | 0.6730          | 0.7095   |
| 0.5827        | 18.0  | 6768  | 0.6602          | 0.7145   |
| 0.5764        | 19.0  | 7144  | 0.6481          | 0.7179   |
| 0.5667        | 20.0  | 7520  | 0.6375          | 0.7312   |
| 0.5598        | 21.0  | 7896  | 0.6271          | 0.7329   |
| 0.4963        | 22.0  | 8272  | 0.6181          | 0.7346   |
| 0.5399        | 23.0  | 8648  | 0.6097          | 0.7396   |
| 0.6005        | 24.0  | 9024  | 0.6025          | 0.7429   |
| 0.5535        | 25.0  | 9400  | 0.5952          | 0.7479   |
| 0.5292        | 26.0  | 9776  | 0.5886          | 0.7513   |
| 0.4834        | 27.0  | 10152 | 0.5826          | 0.7529   |
| 0.4735        | 28.0  | 10528 | 0.5772          | 0.7546   |
| 0.5034        | 29.0  | 10904 | 0.5722          | 0.7563   |
| 0.4846        | 30.0  | 11280 | 0.5675          | 0.7579   |
| 0.5398        | 31.0  | 11656 | 0.5634          | 0.7596   |
| 0.5623        | 32.0  | 12032 | 0.5594          | 0.7646   |
| 0.5122        | 33.0  | 12408 | 0.5557          | 0.7646   |
| 0.483         | 34.0  | 12784 | 0.5523          | 0.7663   |
| 0.4676        | 35.0  | 13160 | 0.5492          | 0.7663   |
| 0.4655        | 36.0  | 13536 | 0.5464          | 0.7679   |
| 0.4231        | 37.0  | 13912 | 0.5438          | 0.7663   |
| 0.5103        | 38.0  | 14288 | 0.5415          | 0.7663   |
| 0.4626        | 39.0  | 14664 | 0.5394          | 0.7679   |
| 0.4372        | 40.0  | 15040 | 0.5375          | 0.7679   |
| 0.496         | 41.0  | 15416 | 0.5357          | 0.7713   |
| 0.4002        | 42.0  | 15792 | 0.5343          | 0.7746   |
| 0.4506        | 43.0  | 16168 | 0.5329          | 0.7746   |
| 0.472         | 44.0  | 16544 | 0.5318          | 0.7746   |
| 0.467         | 45.0  | 16920 | 0.5309          | 0.7763   |
| 0.4861        | 46.0  | 17296 | 0.5301          | 0.7746   |
| 0.4576        | 47.0  | 17672 | 0.5296          | 0.7746   |
| 0.4597        | 48.0  | 18048 | 0.5292          | 0.7763   |
| 0.4465        | 49.0  | 18424 | 0.5290          | 0.7763   |
| 0.3972        | 50.0  | 18800 | 0.5289          | 0.7763   |


### 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