Gluon CV Toolkit
GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.
It is designed for engineers, researchers, and students to fast prototype products and research ideas based on these models. This toolkit offers four main features:
- Training scripts to reproduce SOTA results reported in research papers
- A large number of pre-trained models
- Carefully designed APIs that greatly reduce the implementation complexity
- Community supports
recognize an object in an image.
|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|
detect objects and associate
each pixel inside object area with an
generate visually deceptive images
re-identify pedestrians across scenes
GluonCV supports Python 2.7/3.5 or later. The easiest way to install is via pip.
The following commands install the stable version of GluonCV and MXNet:
pip install gluoncv --upgrade pip install mxnet --upgrade # if cuda 9.2 is installed pip install mxnet-cu92 --upgrade
The latest stable version of GluonCV is 0.3 and depends on mxnet >= 1.3.0
You may get access to latest features and bug fixes with the following commands which install the nightly build of GluonCV and MXNet:
pip install gluoncv --pre --upgrade pip install mxnet --pre --upgrade # if cuda 9.2 is installed pip install mxnet-cu92 --pre --upgrade
There are multiple versions of MXNet pre-built package available. Please refer to mxnet packages if you need more details about MXNet versions.
GluonCV documentation is available at our website.
All tutorials are available at our website!
Check out how to use GluonCV for your own research or projects.
For getting started quickly, refer to notebook runnable examples at Examples.
For advanced examples, check out our Scripts.
For experienced users, check out our API Notes.