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# smids_5x_deit_small_adamax_0001_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: 0.8826
- Accuracy: 0.9132

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2523        | 1.0   | 751   | 0.2969          | 0.8781   |
| 0.098         | 2.0   | 1502  | 0.3056          | 0.9048   |
| 0.0879        | 3.0   | 2253  | 0.3755          | 0.9082   |
| 0.0112        | 4.0   | 3004  | 0.4946          | 0.9015   |
| 0.0331        | 5.0   | 3755  | 0.5182          | 0.9115   |
| 0.034         | 6.0   | 4506  | 0.6894          | 0.8982   |
| 0.0195        | 7.0   | 5257  | 0.5733          | 0.9082   |
| 0.0006        | 8.0   | 6008  | 0.6615          | 0.9065   |
| 0.0           | 9.0   | 6759  | 0.6188          | 0.9149   |
| 0.0001        | 10.0  | 7510  | 0.6672          | 0.9115   |
| 0.0153        | 11.0  | 8261  | 0.6447          | 0.9165   |
| 0.0           | 12.0  | 9012  | 0.7794          | 0.9098   |
| 0.0           | 13.0  | 9763  | 0.7124          | 0.9098   |
| 0.0           | 14.0  | 10514 | 0.7255          | 0.9082   |
| 0.0           | 15.0  | 11265 | 0.7805          | 0.9098   |
| 0.0           | 16.0  | 12016 | 0.8624          | 0.9048   |
| 0.0069        | 17.0  | 12767 | 0.7828          | 0.9115   |
| 0.0001        | 18.0  | 13518 | 0.7977          | 0.9032   |
| 0.0           | 19.0  | 14269 | 0.7469          | 0.9065   |
| 0.0001        | 20.0  | 15020 | 0.8490          | 0.9065   |
| 0.0           | 21.0  | 15771 | 0.7619          | 0.9098   |
| 0.0           | 22.0  | 16522 | 0.7972          | 0.9149   |
| 0.0           | 23.0  | 17273 | 0.7542          | 0.9199   |
| 0.0           | 24.0  | 18024 | 0.8510          | 0.9048   |
| 0.0           | 25.0  | 18775 | 0.8348          | 0.9082   |
| 0.0           | 26.0  | 19526 | 0.8141          | 0.9182   |
| 0.0           | 27.0  | 20277 | 0.8518          | 0.9115   |
| 0.0           | 28.0  | 21028 | 0.8281          | 0.9098   |
| 0.0044        | 29.0  | 21779 | 0.8328          | 0.9132   |
| 0.0           | 30.0  | 22530 | 0.8675          | 0.9149   |
| 0.0           | 31.0  | 23281 | 0.8219          | 0.9048   |
| 0.0           | 32.0  | 24032 | 0.8656          | 0.9065   |
| 0.0           | 33.0  | 24783 | 0.8259          | 0.9048   |
| 0.0           | 34.0  | 25534 | 0.8526          | 0.9082   |
| 0.0           | 35.0  | 26285 | 0.8439          | 0.9098   |
| 0.0           | 36.0  | 27036 | 0.8589          | 0.9115   |
| 0.0           | 37.0  | 27787 | 0.8573          | 0.9149   |
| 0.0           | 38.0  | 28538 | 0.8548          | 0.9149   |
| 0.0           | 39.0  | 29289 | 0.8558          | 0.9149   |
| 0.0           | 40.0  | 30040 | 0.8593          | 0.9149   |
| 0.0           | 41.0  | 30791 | 0.8680          | 0.9149   |
| 0.0           | 42.0  | 31542 | 0.8686          | 0.9149   |
| 0.0           | 43.0  | 32293 | 0.8703          | 0.9132   |
| 0.0           | 44.0  | 33044 | 0.8724          | 0.9132   |
| 0.0           | 45.0  | 33795 | 0.8746          | 0.9132   |
| 0.0           | 46.0  | 34546 | 0.8749          | 0.9132   |
| 0.0           | 47.0  | 35297 | 0.8795          | 0.9132   |
| 0.0           | 48.0  | 36048 | 0.8807          | 0.9132   |
| 0.0           | 49.0  | 36799 | 0.8817          | 0.9132   |
| 0.0           | 50.0  | 37550 | 0.8826          | 0.9132   |


### 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