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Author
Last Commit
May. 22, 2019
Created
Feb. 26, 2018

Gluon CV Toolkit

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| Installation | Documentation | Tutorials |

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:

  1. Training scripts to reproduce SOTA results reported in research papers
  2. A large number of pre-trained models
  3. Carefully designed APIs that greatly reduce the implementation complexity
  4. Community supports

Supported Applications

Application Illustration Available Models
Image Classification:
recognize an object in an image.
classification 50+ models, including
ResNet, MobileNet,
DenseNet, VGG, ...
Object Detection:
detect multiple objects with their
bounding boxes in an image.
detection Faster RCNN, SSD, Yolo-v3
Semantic Segmentation:
associate each pixel of an image
with a categorical label.
semantic FCN, PSP, DeepLab v3
Instance Segmentation:
detect objects and associate
each pixel inside object area with an
instance label.
instance Mask RCNN
Pose Estimation:
detect human pose
from images.
pose Simple Pose
GAN:
generate visually deceptive images
lsun WGAN, CycleGAN
Person Re-ID:
re-identify pedestrians across scenes
re-id Market1501 baseline

Installation

GluonCV supports Python 2.7/3.5 or later. The easiest way to install is via pip.

Stable Release

The following commands install the stable version of GluonCV and MXNet:

pip install gluoncv --upgrade
pip install mxnet-mkl --upgrade
# if cuda 9.2 is installed
pip install mxnet-cu92mkl --upgrade

The latest stable version of GluonCV is 0.4 and depends on mxnet >= 1.4.0

Nightly Release

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-mkl --pre --upgrade
# if cuda 9.2 is installed
pip install mxnet-cu92mkl --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.

Docs đź“–

GluonCV documentation is available at our website.

Examples

All tutorials are available at our website!

Resources

Check out how to use GluonCV for your own research or projects.

Citation

If you feel our code or models helps in your research, please kindly cite our papers:

@article{he2018bag,
  title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
  author={He, Tong and Zhang, Zhi and Zhang, Hang and Zhang, Zhongyue and Xie, Junyuan and Li, Mu},
  journal={arXiv preprint arXiv:1812.01187},
  year={2018}
}

@article{zhang2019bag,
  title={Bag of Freebies for Training Object Detection Neural Networks},
  author={Zhang, Zhi and He, Tong and Zhang, Hang and Zhang, Zhongyue and Xie, Junyuan and Li, Mu},
  journal={arXiv preprint arXiv:1902.04103},
  year={2019}
}```

Latest Releases
GluonCV toolkit v0.4.0
 Mar. 26 2019
GluonCV toolkit v0.4.0
 Mar. 19 2019
Gluon CV Toolkit 0.3.0
 Oct. 16 2018
Gluon CV Toolkit v0.2.0
 Jun. 25 2018
Gluon CV Toolkit v0.1
 Apr. 30 2018