InstaBoost
This repository is implementation of ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting". Our paper has been released on arXiv https://arxiv.org/abs/1908.07801.
Install InstaBoost
- Requirements
We implement our method on Python 3.5. To install InstaBoost, use this command.
pip install instaboost
The detail implementation can be found here.
Quick Start
Currently we have integrated InstaBoost into three open implementations: mmdetection, detectron and yolact.
-
mmdetection: Checkout mmdetection.
-
detectron: Checkout detectron.
-
yolact: Checkout yolact
Since these frameworks may continue updating, codes in this repo may be a little different from their current repo.
Use InstaBoost In Your Project
It is easy to integrate InstaBoost into your framework. You can refer to instructions of our implementations here, here and here
Setup InstaBoost Configurations
To change InstaBoost Configurations, users can use function InstaBoostConfig.
Model Zoo
Results and models are available in the Model zoo. More models are coming!
Citation
If you use this toolbox or benchmark in your research, please cite this project.
@article{Fang2019InstaBoost,
author = {Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu},
title = {InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting},
journal={arXiv preprint arXiv:1908.07801},
year = {2019}
}
Please also cite mmdetection, detectron and yolact if you use the corresponding codes.
Acknowledgement
Our detection and instance segmentation framework is based on mmdetecion, detectron and yolact.