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# smids_5x_deit_tiny_adamax_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: 0.9489
- Accuracy: 0.8898

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3549        | 1.0   | 376   | 0.4844          | 0.8264   |
| 0.2678        | 2.0   | 752   | 0.3259          | 0.8798   |
| 0.3098        | 3.0   | 1128  | 0.3469          | 0.8548   |
| 0.2057        | 4.0   | 1504  | 0.3089          | 0.8831   |
| 0.15          | 5.0   | 1880  | 0.4280          | 0.8748   |
| 0.0947        | 6.0   | 2256  | 0.5773          | 0.8581   |
| 0.1544        | 7.0   | 2632  | 0.3805          | 0.8881   |
| 0.1085        | 8.0   | 3008  | 0.4878          | 0.8731   |
| 0.0399        | 9.0   | 3384  | 0.4495          | 0.8965   |
| 0.0251        | 10.0  | 3760  | 0.5573          | 0.8681   |
| 0.0684        | 11.0  | 4136  | 0.4467          | 0.8648   |
| 0.0506        | 12.0  | 4512  | 0.5126          | 0.8982   |
| 0.0075        | 13.0  | 4888  | 0.8575          | 0.8715   |
| 0.0481        | 14.0  | 5264  | 0.7463          | 0.8664   |
| 0.0077        | 15.0  | 5640  | 0.6816          | 0.8865   |
| 0.0098        | 16.0  | 6016  | 0.6312          | 0.8831   |
| 0.0003        | 17.0  | 6392  | 0.7022          | 0.8965   |
| 0.0075        | 18.0  | 6768  | 0.6976          | 0.8731   |
| 0.0042        | 19.0  | 7144  | 0.6012          | 0.8881   |
| 0.0311        | 20.0  | 7520  | 0.7693          | 0.8932   |
| 0.003         | 21.0  | 7896  | 0.6254          | 0.8915   |
| 0.0101        | 22.0  | 8272  | 0.6004          | 0.8998   |
| 0.0209        | 23.0  | 8648  | 0.7643          | 0.8815   |
| 0.0001        | 24.0  | 9024  | 0.8262          | 0.8848   |
| 0.0007        | 25.0  | 9400  | 0.6944          | 0.8898   |
| 0.0034        | 26.0  | 9776  | 0.7140          | 0.8915   |
| 0.0071        | 27.0  | 10152 | 0.8088          | 0.8798   |
| 0.0001        | 28.0  | 10528 | 0.7766          | 0.9032   |
| 0.0039        | 29.0  | 10904 | 0.8084          | 0.8948   |
| 0.0045        | 30.0  | 11280 | 0.7741          | 0.8831   |
| 0.0006        | 31.0  | 11656 | 0.8264          | 0.8932   |
| 0.0           | 32.0  | 12032 | 0.8432          | 0.8865   |
| 0.0           | 33.0  | 12408 | 0.8641          | 0.8848   |
| 0.0           | 34.0  | 12784 | 0.8447          | 0.8865   |
| 0.0           | 35.0  | 13160 | 0.8402          | 0.8848   |
| 0.0           | 36.0  | 13536 | 0.8232          | 0.8948   |
| 0.0           | 37.0  | 13912 | 0.8382          | 0.8915   |
| 0.0           | 38.0  | 14288 | 0.8652          | 0.8898   |
| 0.0           | 39.0  | 14664 | 0.8733          | 0.8848   |
| 0.0           | 40.0  | 15040 | 0.8254          | 0.8881   |
| 0.0           | 41.0  | 15416 | 0.8627          | 0.8848   |
| 0.0           | 42.0  | 15792 | 0.8799          | 0.8881   |
| 0.0           | 43.0  | 16168 | 0.8887          | 0.8915   |
| 0.0           | 44.0  | 16544 | 0.9046          | 0.8932   |
| 0.0           | 45.0  | 16920 | 0.9092          | 0.8932   |
| 0.0031        | 46.0  | 17296 | 0.9143          | 0.8881   |
| 0.0           | 47.0  | 17672 | 0.9293          | 0.8915   |
| 0.0           | 48.0  | 18048 | 0.9378          | 0.8898   |
| 0.0           | 49.0  | 18424 | 0.9447          | 0.8898   |
| 0.0023        | 50.0  | 18800 | 0.9489          | 0.8898   |


### 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