vebits_api
A high-level, comprehensive package that leverages user's experience when working with Tensorflow's Object Detection API.
Overview
This package has been developed to turn my works at Vebits into a friendly, easy-to-use API that facilitate user's experience when working with Tensorflow Object Detection API. New features are being developed and tested to working with DarkNet/Darkflow for training YOLO models running real-time on mobile devices; as well as with MMdetection for high-performance, highly scalable object detection toolbox.
Dependencies
All dependencies are listed under requirement.txt
certifi==2019.6.16
cycler==0.10.0
decorator==4.4.0
imageio==2.5.0
imgaug==0.2.9
imutils==0.5.2
kiwisolver==1.1.0
matplotlib==3.1.1
networkx==2.3
numpy==1.16.4
opencv-python==4.1.0.25
pandas==0.25.0
Pillow==6.1.0
protobuf==3.9.0
pyparsing==2.4.1
python-dateutil==2.8.0
pytz==2019.1
PyWavelets==1.0.3
scikit-image==0.15.0
scipy==1.3.0
Shapely==1.6.4.post2
six==1.12.0
tqdm==4.32.2
Optionally, the following packages are required for the API to work seamlessly with
- Tensorflow's Object Detection API:
tensorflow
pip install tensorflow-gpu
- Darknet/Darkflow for YOLO models:
darkflow
git clone https://github.com/thtrieu/darkflow.git
cd darkflow
pip install -e .
- MMdetection toolbox: details on installation can be found here.
Installation
To install the latest stable release of this package, simply run:
pip install vebits_api
Alternatively, to build the project from source in development mode and allow the changes to take effect immediately:
git clone https://github.com/hnt4499/vebits_api/
cd vebits_api/
pip install -e .
or
pip install git+https://github.com/hnt4499/vebits_api.git
That's it! To make use of available scripts for data manipulating/processing/visualization, simply copy all scripts under scripts
folder to your working directory.
TODO:
- Complete README.md: Requirements, Build from source, Usage, Reference, Examples.
- Incorporate DarkNet into this package.
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