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# smids_10x_deit_small_adamax_00001_fold5

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: 0.8990
- Accuracy: 0.8967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2314        | 1.0   | 750   | 0.3103          | 0.8783   |
| 0.2018        | 2.0   | 1500  | 0.2701          | 0.8983   |
| 0.1509        | 3.0   | 2250  | 0.2783          | 0.9      |
| 0.0994        | 4.0   | 3000  | 0.3003          | 0.8983   |
| 0.1044        | 5.0   | 3750  | 0.3361          | 0.905    |
| 0.0438        | 6.0   | 4500  | 0.4330          | 0.89     |
| 0.017         | 7.0   | 5250  | 0.4929          | 0.9      |
| 0.0271        | 8.0   | 6000  | 0.5583          | 0.8883   |
| 0.0025        | 9.0   | 6750  | 0.6641          | 0.895    |
| 0.0019        | 10.0  | 7500  | 0.6642          | 0.895    |
| 0.0069        | 11.0  | 8250  | 0.7364          | 0.895    |
| 0.0001        | 12.0  | 9000  | 0.7531          | 0.8917   |
| 0.0001        | 13.0  | 9750  | 0.7730          | 0.8983   |
| 0.0           | 14.0  | 10500 | 0.7588          | 0.895    |
| 0.0           | 15.0  | 11250 | 0.8009          | 0.8933   |
| 0.0           | 16.0  | 12000 | 0.8380          | 0.8883   |
| 0.002         | 17.0  | 12750 | 0.8384          | 0.8917   |
| 0.0           | 18.0  | 13500 | 0.8173          | 0.9017   |
| 0.0           | 19.0  | 14250 | 0.8127          | 0.8983   |
| 0.0           | 20.0  | 15000 | 0.8957          | 0.8917   |
| 0.0           | 21.0  | 15750 | 0.8587          | 0.8917   |
| 0.0           | 22.0  | 16500 | 0.8745          | 0.89     |
| 0.0           | 23.0  | 17250 | 0.8882          | 0.8917   |
| 0.0           | 24.0  | 18000 | 0.8669          | 0.895    |
| 0.0           | 25.0  | 18750 | 0.8973          | 0.8917   |
| 0.0           | 26.0  | 19500 | 0.8589          | 0.895    |
| 0.0           | 27.0  | 20250 | 0.8360          | 0.9      |
| 0.0           | 28.0  | 21000 | 0.8787          | 0.8967   |
| 0.0           | 29.0  | 21750 | 0.8640          | 0.895    |
| 0.0039        | 30.0  | 22500 | 0.8923          | 0.8867   |
| 0.0           | 31.0  | 23250 | 0.8650          | 0.8967   |
| 0.0           | 32.0  | 24000 | 0.8868          | 0.8983   |
| 0.0           | 33.0  | 24750 | 0.8702          | 0.8967   |
| 0.0           | 34.0  | 25500 | 0.8705          | 0.8933   |
| 0.0           | 35.0  | 26250 | 0.8693          | 0.8933   |
| 0.0           | 36.0  | 27000 | 0.8764          | 0.895    |
| 0.0035        | 37.0  | 27750 | 0.8971          | 0.8933   |
| 0.0           | 38.0  | 28500 | 0.8853          | 0.895    |
| 0.0           | 39.0  | 29250 | 0.8831          | 0.895    |
| 0.0           | 40.0  | 30000 | 0.8852          | 0.895    |
| 0.0           | 41.0  | 30750 | 0.8936          | 0.8967   |
| 0.0           | 42.0  | 31500 | 0.8989          | 0.895    |
| 0.0           | 43.0  | 32250 | 0.8952          | 0.895    |
| 0.0           | 44.0  | 33000 | 0.8982          | 0.895    |
| 0.0           | 45.0  | 33750 | 0.8981          | 0.895    |
| 0.0           | 46.0  | 34500 | 0.8978          | 0.895    |
| 0.0           | 47.0  | 35250 | 0.8987          | 0.895    |
| 0.0           | 48.0  | 36000 | 0.8989          | 0.895    |
| 0.0           | 49.0  | 36750 | 0.8995          | 0.895    |
| 0.0           | 50.0  | 37500 | 0.8990          | 0.8967   |


### 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