deepaugment 0.1.0

Discover augmentation strategies tailored for your data

# DeepAugment

Discover best augmentation strategies for your dataset.

**Benefits**
- Boosts accuracy of deep learning models upto +X% compared to models with non-augmented data, and +Y% compared to models with manually augmented data.
- Saves weeks of time by automating data augmentation process, which normally takes lots of trial & error.

## Installation & Usage
```console
$ pip install deepaugment
```

```Python
import deepaugment

best_policies = DeepAugment(my_data, my_labels)

my_augmented_data = daug.apply(my_data, best_policies)
```

## Results with CIFAR-10, ImageNet, & blabla

## How it works?
DeepAugment working method can be disected into three areas:
1. Search space of augmentation
2. Optimizer
3. Child model

### 1. Search space of augmentation
### 2. Optimizer
### 3. Child model


### Repo Organization

├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│   ├── external <- Data from third party sources.
│   ├── interim <- Intermediate data that has been transformed.
│   ├── processed <- The final, canonical data sets for modeling.
│   └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│   ├── __init__.py <- Makes src a Python module
│ │
│   ├── data <- Scripts to download or generate data
│   │   └── make_dataset.py
│ │
│   ├── features <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│ │
│   ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│ │
│   └── visualization <- Scripts to create exploratory and results oriented visualizations
│   └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org


--------

<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>

## Contact
Baris Ozmen, [e-mail]