fidler-lab / polyrnn-pp

Inference Code for Polygon-RNN++ (CVPR 2018)

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool


This is the official inference code for Polygon-RNN++ (CVPR-2018). For technical details, please refer to:

An official pytorch reimplementation with training/tool code is available here

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
David Acuna*, Huan Ling*, Amlan Kar*, Sanja Fidler (* denotes equal contribution)
CVPR 2018
[Paper] [Video] [Project Page] [Demo] [Training/Tool Code] Model


  1. Clone the repository
git clone && cd polyrnn
  1. Install dependencies
    (Note: Using a GPU (and tensorflow-gpu) is recommended. The model will run on a CPU, albeit slowly.)
virtualenv env
source env/bin/activate
pip install -r requirements.txt
  1. Download the pre-trained models and graphs (448 MB)
    (These models were trained on the Cityscapes Dataset)
  1. Run

This should produce results in the output/ folder that look like ex2 ex1


Checkout the ipython notebook that provides a simple walkthrough demonstrating how to run our model on sample input image crops

If you use this code, please cite:

title={Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++},
author={David Acuna and Huan Ling and Amlan Kar and Sanja Fidler},

title = {Annotating Object Instances with a Polygon-RNN},
author = {Lluis Castrejon and Kaustav Kundu and Raquel Urtasun and Sanja Fidler},
booktitle = {CVPR},
year = {2017}
ezoic increase your site revenue


Inference Code for Polygon-RNN++ (CVPR 2018)

License:GNU General Public License v3.0


Language:Jupyter Notebook 88.5%Language:Python 11.2%Language:Shell 0.3%