Metadata-Version: 2.1
Name: deepaugment
Version: 0.1.0
Summary: Discover augmentation strategies tailored for your data
Home-page: https://github.com/barisozmen/deepaugment
Author: Baris Ozmen
Author-email: hbaristr@gmail.com
License: BSD
Download-URL: https://github.com/barisozmen/deepaugment/tarball/0.1.0
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Requires-Dist: click
Requires-Dist: Sphinx
Requires-Dist: coverage
Requires-Dist: awscli
Requires-Dist: flake8
Requires-Dist: python-dotenv (>=0.5.1)
Requires-Dist: six
Requires-Dist: numpy (<1.16,>=1.15)
Requires-Dist: scipy
Requires-Dist: Pillow
Requires-Dist: matplotlib
Requires-Dist: scikit-image
Requires-Dist: opencv-python
Requires-Dist: imageio
Requires-Dist: Shapely
Requires-Dist: imgaug
Requires-Dist: opencv-contrib-python
Requires-Dist: scikit-optimize
Requires-Dist: sphinx
Requires-Dist: sphinx-rtd-theme
Requires-Dist: nose
Requires-Dist: pypi-publisher

# 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, hbaristr@gmail.com


