A high-level, comprehensive package that leverages user's experience when working with Tensorflow's Object Detection API.
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.
All dependencies are listed under
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==184.108.40.206 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:
pip install tensorflow-gpu
- Darknet/Darkflow for YOLO models:
git clone https://github.com/thtrieu/darkflow.git cd darkflow pip install -e .
- MMdetection toolbox: details on installation can be found here.
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 .
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.
- Complete README.md: Requirements, Build from source, Usage, Reference, Examples.
- Incorporate DarkNet into this package.
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