GluonCV: a Deep Learning Toolkit for Computer Vision¶
GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision.
- training scripts that reproduce SOTA results reported in latest papers,
- a large set of pre-trained models,
- carefully designed APIs and easy to understand implementations,
- community support.
recognize an object in
|50+ models, including
DenseNet, VGG, ...
detect multiple objects
with their bounding boxes
in an image.
|Faster RCNN, SSD, Yolo-v3|
associate each pixel
of an image with
a categorical label.
|FCN, PSP, DeepLab v3|
associate each pixel of
an image with
an instance label.
GluonCV depends on the recent version of MXNet. The easiest way to install MXNet is through pip. The following command installs CPU version of MXNet.
# the oldest stable version of mxnet required is 1.3.0 pip install mxnet>=1.3.0 --upgrade # you can install nightly build of mxnet to access up-to-date features pip install --pre --upgrade mxnet
The easiest way to install GluonCV is through pip.
pip install gluoncv --upgrade # if you are eager to try new features, try nightly build instead pip install gluoncv --pre --upgrade
Nightly build is updated daily around 12am UTC to match master progress.
Optionally, you can clone the GluonCV project and install it locally
git clone https://github.com/dmlc/gluon-cv cd gluon-cv && python setup.py install --user