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# smids_10x_deit_small_rms_001_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: 1.5007
- Accuracy: 0.8063

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7661        | 1.0   | 751   | 0.8852          | 0.6060   |
| 0.6485        | 2.0   | 1502  | 0.7308          | 0.6578   |
| 0.696         | 3.0   | 2253  | 0.7036          | 0.6594   |
| 0.6301        | 4.0   | 3004  | 0.7247          | 0.6761   |
| 0.6536        | 5.0   | 3755  | 0.6760          | 0.6828   |
| 0.6653        | 6.0   | 4506  | 0.6159          | 0.7095   |
| 0.5636        | 7.0   | 5257  | 0.5571          | 0.7579   |
| 0.5506        | 8.0   | 6008  | 0.6121          | 0.7329   |
| 0.5582        | 9.0   | 6759  | 0.5862          | 0.7546   |
| 0.5548        | 10.0  | 7510  | 0.5892          | 0.7329   |
| 0.5549        | 11.0  | 8261  | 0.5848          | 0.7412   |
| 0.5362        | 12.0  | 9012  | 0.6200          | 0.7396   |
| 0.4966        | 13.0  | 9763  | 0.5530          | 0.7713   |
| 0.4818        | 14.0  | 10514 | 0.5786          | 0.7529   |
| 0.4746        | 15.0  | 11265 | 0.6115          | 0.7229   |
| 0.4852        | 16.0  | 12016 | 0.6019          | 0.7362   |
| 0.4634        | 17.0  | 12767 | 0.5783          | 0.7613   |
| 0.453         | 18.0  | 13518 | 0.5821          | 0.7462   |
| 0.4908        | 19.0  | 14269 | 0.5445          | 0.7629   |
| 0.4881        | 20.0  | 15020 | 0.5377          | 0.7763   |
| 0.4025        | 21.0  | 15771 | 0.5423          | 0.7813   |
| 0.4591        | 22.0  | 16522 | 0.5168          | 0.7813   |
| 0.3695        | 23.0  | 17273 | 0.5306          | 0.7730   |
| 0.4288        | 24.0  | 18024 | 0.5369          | 0.7997   |
| 0.4022        | 25.0  | 18775 | 0.5176          | 0.7896   |
| 0.3916        | 26.0  | 19526 | 0.5681          | 0.7830   |
| 0.4188        | 27.0  | 20277 | 0.5488          | 0.7830   |
| 0.4088        | 28.0  | 21028 | 0.5430          | 0.7947   |
| 0.3236        | 29.0  | 21779 | 0.5528          | 0.7947   |
| 0.3272        | 30.0  | 22530 | 0.5104          | 0.8164   |
| 0.305         | 31.0  | 23281 | 0.5401          | 0.8080   |
| 0.3925        | 32.0  | 24032 | 0.5133          | 0.8013   |
| 0.3211        | 33.0  | 24783 | 0.5292          | 0.7980   |
| 0.2648        | 34.0  | 25534 | 0.6583          | 0.7846   |
| 0.2286        | 35.0  | 26285 | 0.6241          | 0.7896   |
| 0.2863        | 36.0  | 27036 | 0.6657          | 0.7947   |
| 0.2968        | 37.0  | 27787 | 0.5922          | 0.8214   |
| 0.2233        | 38.0  | 28538 | 0.6706          | 0.7880   |
| 0.1424        | 39.0  | 29289 | 0.6769          | 0.8097   |
| 0.2253        | 40.0  | 30040 | 0.7552          | 0.7963   |
| 0.1253        | 41.0  | 30791 | 0.7804          | 0.8164   |
| 0.16          | 42.0  | 31542 | 0.8311          | 0.7980   |
| 0.1962        | 43.0  | 32293 | 0.8198          | 0.8047   |
| 0.0759        | 44.0  | 33044 | 0.9444          | 0.7997   |
| 0.1175        | 45.0  | 33795 | 0.9448          | 0.8080   |
| 0.1291        | 46.0  | 34546 | 1.0860          | 0.8080   |
| 0.0879        | 47.0  | 35297 | 1.2492          | 0.7980   |
| 0.0404        | 48.0  | 36048 | 1.3416          | 0.8047   |
| 0.0466        | 49.0  | 36799 | 1.4861          | 0.8030   |
| 0.0362        | 50.0  | 37550 | 1.5007          | 0.8063   |


### 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