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# smids_3x_deit_tiny_rms_001_fold3

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: 0.7451
- Accuracy: 0.7017

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0251        | 1.0   | 225   | 0.9264          | 0.5433   |
| 0.8762        | 2.0   | 450   | 0.9033          | 0.5383   |
| 0.9046        | 3.0   | 675   | 0.8597          | 0.52     |
| 0.8168        | 4.0   | 900   | 0.8502          | 0.5867   |
| 0.8182        | 5.0   | 1125  | 0.8396          | 0.5417   |
| 0.8366        | 6.0   | 1350  | 0.8032          | 0.64     |
| 0.7873        | 7.0   | 1575  | 0.7872          | 0.6367   |
| 0.8222        | 8.0   | 1800  | 0.7892          | 0.605    |
| 0.8096        | 9.0   | 2025  | 0.8074          | 0.63     |
| 0.7866        | 10.0  | 2250  | 0.8155          | 0.5667   |
| 0.7895        | 11.0  | 2475  | 0.7692          | 0.6433   |
| 0.7721        | 12.0  | 2700  | 0.8106          | 0.6017   |
| 0.781         | 13.0  | 2925  | 0.7742          | 0.6533   |
| 0.7888        | 14.0  | 3150  | 0.7929          | 0.6117   |
| 0.7617        | 15.0  | 3375  | 0.7600          | 0.6683   |
| 0.8324        | 16.0  | 3600  | 0.7701          | 0.6433   |
| 0.7598        | 17.0  | 3825  | 0.8095          | 0.6333   |
| 0.7476        | 18.0  | 4050  | 0.7803          | 0.6033   |
| 0.7071        | 19.0  | 4275  | 0.7505          | 0.6683   |
| 0.7193        | 20.0  | 4500  | 0.7784          | 0.6183   |
| 0.6927        | 21.0  | 4725  | 0.7879          | 0.6467   |
| 0.666         | 22.0  | 4950  | 0.7212          | 0.6967   |
| 0.6763        | 23.0  | 5175  | 0.7194          | 0.6833   |
| 0.6715        | 24.0  | 5400  | 0.7919          | 0.6367   |
| 0.7294        | 25.0  | 5625  | 0.7785          | 0.6733   |
| 0.6936        | 26.0  | 5850  | 0.7216          | 0.6983   |
| 0.6322        | 27.0  | 6075  | 0.8000          | 0.6833   |
| 0.6761        | 28.0  | 6300  | 0.7942          | 0.6183   |
| 0.688         | 29.0  | 6525  | 0.7281          | 0.6567   |
| 0.6228        | 30.0  | 6750  | 0.7332          | 0.6567   |
| 0.6366        | 31.0  | 6975  | 0.7601          | 0.6717   |
| 0.6176        | 32.0  | 7200  | 0.7157          | 0.6883   |
| 0.6636        | 33.0  | 7425  | 0.7555          | 0.6567   |
| 0.6315        | 34.0  | 7650  | 0.7242          | 0.665    |
| 0.5915        | 35.0  | 7875  | 0.6940          | 0.6783   |
| 0.6259        | 36.0  | 8100  | 0.6760          | 0.6917   |
| 0.6325        | 37.0  | 8325  | 0.6834          | 0.6967   |
| 0.5846        | 38.0  | 8550  | 0.7137          | 0.6733   |
| 0.6018        | 39.0  | 8775  | 0.6801          | 0.6933   |
| 0.5692        | 40.0  | 9000  | 0.6837          | 0.6883   |
| 0.5234        | 41.0  | 9225  | 0.6917          | 0.6833   |
| 0.5543        | 42.0  | 9450  | 0.6614          | 0.7017   |
| 0.5363        | 43.0  | 9675  | 0.6720          | 0.7017   |
| 0.5474        | 44.0  | 9900  | 0.6703          | 0.7067   |
| 0.5234        | 45.0  | 10125 | 0.7035          | 0.6983   |
| 0.4923        | 46.0  | 10350 | 0.7111          | 0.7017   |
| 0.5435        | 47.0  | 10575 | 0.6985          | 0.7133   |
| 0.4932        | 48.0  | 10800 | 0.7085          | 0.7133   |
| 0.486         | 49.0  | 11025 | 0.7485          | 0.7033   |
| 0.4701        | 50.0  | 11250 | 0.7451          | 0.7017   |


### 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