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# smids_10x_deit_base_adamax_00001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8741
- Accuracy: 0.8902

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.216         | 1.0   | 750   | 0.2934          | 0.8852   |
| 0.1089        | 2.0   | 1500  | 0.3003          | 0.8885   |
| 0.0954        | 3.0   | 2250  | 0.3369          | 0.8852   |
| 0.0518        | 4.0   | 3000  | 0.4029          | 0.8902   |
| 0.0792        | 5.0   | 3750  | 0.4723          | 0.8819   |
| 0.0261        | 6.0   | 4500  | 0.5460          | 0.8885   |
| 0.0158        | 7.0   | 5250  | 0.6663          | 0.8819   |
| 0.0057        | 8.0   | 6000  | 0.6585          | 0.8802   |
| 0.0002        | 9.0   | 6750  | 0.6982          | 0.8918   |
| 0.0006        | 10.0  | 7500  | 0.7734          | 0.8918   |
| 0.0           | 11.0  | 8250  | 0.7967          | 0.8852   |
| 0.0           | 12.0  | 9000  | 0.8312          | 0.8885   |
| 0.0001        | 13.0  | 9750  | 0.7897          | 0.8869   |
| 0.0           | 14.0  | 10500 | 0.8305          | 0.8885   |
| 0.0           | 15.0  | 11250 | 0.7993          | 0.8869   |
| 0.0           | 16.0  | 12000 | 0.8582          | 0.8835   |
| 0.0           | 17.0  | 12750 | 0.8331          | 0.8902   |
| 0.0           | 18.0  | 13500 | 0.8238          | 0.8952   |
| 0.0           | 19.0  | 14250 | 0.8345          | 0.8952   |
| 0.0           | 20.0  | 15000 | 0.8413          | 0.8885   |
| 0.0           | 21.0  | 15750 | 0.8573          | 0.8918   |
| 0.0085        | 22.0  | 16500 | 0.8363          | 0.8918   |
| 0.005         | 23.0  | 17250 | 0.8431          | 0.8885   |
| 0.0           | 24.0  | 18000 | 0.8537          | 0.8902   |
| 0.0           | 25.0  | 18750 | 0.8479          | 0.8918   |
| 0.0           | 26.0  | 19500 | 0.8197          | 0.8869   |
| 0.0098        | 27.0  | 20250 | 0.8247          | 0.8869   |
| 0.0           | 28.0  | 21000 | 0.8273          | 0.8885   |
| 0.0           | 29.0  | 21750 | 0.8481          | 0.8918   |
| 0.0           | 30.0  | 22500 | 0.8486          | 0.8885   |
| 0.0           | 31.0  | 23250 | 0.8913          | 0.8935   |
| 0.0           | 32.0  | 24000 | 0.8769          | 0.8918   |
| 0.0           | 33.0  | 24750 | 0.8699          | 0.8885   |
| 0.0           | 34.0  | 25500 | 0.8861          | 0.8935   |
| 0.0           | 35.0  | 26250 | 0.8555          | 0.8852   |
| 0.0           | 36.0  | 27000 | 0.8657          | 0.8918   |
| 0.0099        | 37.0  | 27750 | 0.8602          | 0.8902   |
| 0.0           | 38.0  | 28500 | 0.8913          | 0.8918   |
| 0.0           | 39.0  | 29250 | 0.8649          | 0.8885   |
| 0.0           | 40.0  | 30000 | 0.8620          | 0.8885   |
| 0.0           | 41.0  | 30750 | 0.8685          | 0.8885   |
| 0.0           | 42.0  | 31500 | 0.8731          | 0.8902   |
| 0.0           | 43.0  | 32250 | 0.8772          | 0.8902   |
| 0.0           | 44.0  | 33000 | 0.8742          | 0.8902   |
| 0.0026        | 45.0  | 33750 | 0.8773          | 0.8902   |
| 0.0           | 46.0  | 34500 | 0.8745          | 0.8902   |
| 0.0           | 47.0  | 35250 | 0.8728          | 0.8885   |
| 0.0           | 48.0  | 36000 | 0.8716          | 0.8885   |
| 0.0           | 49.0  | 36750 | 0.8740          | 0.8885   |
| 0.0           | 50.0  | 37500 | 0.8741          | 0.8902   |


### 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