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# smids_1x_deit_tiny_rms_001_fold2

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: 1.2712
- Accuracy: 0.7554

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1905        | 1.0   | 75   | 1.0709          | 0.4526   |
| 1.1189        | 2.0   | 150  | 0.9655          | 0.5241   |
| 0.9756        | 3.0   | 225  | 0.9009          | 0.4626   |
| 0.9398        | 4.0   | 300  | 0.8912          | 0.5757   |
| 0.9083        | 5.0   | 375  | 0.8740          | 0.5191   |
| 0.9033        | 6.0   | 450  | 0.8827          | 0.5092   |
| 0.8824        | 7.0   | 525  | 0.7939          | 0.5824   |
| 0.9323        | 8.0   | 600  | 0.8247          | 0.5524   |
| 0.8419        | 9.0   | 675  | 0.8138          | 0.5807   |
| 0.8164        | 10.0  | 750  | 0.8013          | 0.5674   |
| 0.859         | 11.0  | 825  | 0.8565          | 0.5458   |
| 0.7125        | 12.0  | 900  | 0.8056          | 0.6173   |
| 0.803         | 13.0  | 975  | 0.8062          | 0.6190   |
| 0.7687        | 14.0  | 1050 | 0.7699          | 0.6040   |
| 0.7356        | 15.0  | 1125 | 0.7299          | 0.6589   |
| 0.7325        | 16.0  | 1200 | 0.7012          | 0.6722   |
| 0.6848        | 17.0  | 1275 | 0.7155          | 0.6423   |
| 0.6885        | 18.0  | 1350 | 0.6752          | 0.6689   |
| 0.6698        | 19.0  | 1425 | 0.6669          | 0.6872   |
| 0.7037        | 20.0  | 1500 | 0.6890          | 0.6789   |
| 0.6983        | 21.0  | 1575 | 0.7382          | 0.6423   |
| 0.6127        | 22.0  | 1650 | 0.6732          | 0.7038   |
| 0.6242        | 23.0  | 1725 | 0.6106          | 0.7304   |
| 0.658         | 24.0  | 1800 | 0.6268          | 0.7121   |
| 0.5546        | 25.0  | 1875 | 0.6631          | 0.7088   |
| 0.5765        | 26.0  | 1950 | 0.6682          | 0.6988   |
| 0.6162        | 27.0  | 2025 | 0.6203          | 0.7304   |
| 0.5296        | 28.0  | 2100 | 0.6174          | 0.7438   |
| 0.5276        | 29.0  | 2175 | 0.5823          | 0.7371   |
| 0.4954        | 30.0  | 2250 | 0.7129          | 0.6922   |
| 0.5509        | 31.0  | 2325 | 0.6075          | 0.7404   |
| 0.4629        | 32.0  | 2400 | 0.6387          | 0.7488   |
| 0.4323        | 33.0  | 2475 | 0.6167          | 0.7421   |
| 0.4094        | 34.0  | 2550 | 0.6489          | 0.7637   |
| 0.419         | 35.0  | 2625 | 0.6362          | 0.7371   |
| 0.444         | 36.0  | 2700 | 0.6255          | 0.7621   |
| 0.4294        | 37.0  | 2775 | 0.6272          | 0.7604   |
| 0.3866        | 38.0  | 2850 | 0.6218          | 0.7770   |
| 0.3776        | 39.0  | 2925 | 0.6660          | 0.7637   |
| 0.3382        | 40.0  | 3000 | 0.7027          | 0.7720   |
| 0.3406        | 41.0  | 3075 | 0.7627          | 0.7770   |
| 0.3115        | 42.0  | 3150 | 0.7813          | 0.7737   |
| 0.2146        | 43.0  | 3225 | 0.8652          | 0.7521   |
| 0.2529        | 44.0  | 3300 | 0.9528          | 0.7504   |
| 0.1582        | 45.0  | 3375 | 0.9733          | 0.7704   |
| 0.1575        | 46.0  | 3450 | 1.0460          | 0.7704   |
| 0.1254        | 47.0  | 3525 | 1.1356          | 0.7737   |
| 0.076         | 48.0  | 3600 | 1.2020          | 0.7604   |
| 0.0433        | 49.0  | 3675 | 1.2652          | 0.7571   |
| 0.0823        | 50.0  | 3750 | 1.2712          | 0.7554   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm