README.md

CARAFE: Content-Aware ReAssembly of FEatures

Introduction

We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures.

@inproceedings{Wang_2019_ICCV,
    title = {CARAFE: Content-Aware ReAssembly of FEatures},
    author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {October},
    year = {2019}
}

Results and Models

The results on COCO 2017 val is shown in the below table.

Method	Backbone	Style	Lr schd	Test Proposal Num	Box AP	Mask AP	Download
Faster R-CNN w/ CARAFE	R-50-FPN	pytorch	1x	1000	37.8	-	model
-	-	-	-	2000	37.9	-	-
Mask R-CNN w/ CARAFE	R-50-FPN	pytorch	1x	1000	38.6	35.6	model
-	-	-	-	2000	38.6	35.7	-

Implementation

The CUDA implementation of CARAFE can be find at mmdet/ops/carafe under this repository.

Setup CARAFE

a. Use CARAFE in mmdetection.

Install mmdetection following the official guide.

b. Use CARAFE in your own project.

Git clone mmdetection.

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection

Setup CARAFE in our project.

cp -r ./mmdet/ops/carafe $Your_Project_Path$
cd $Your_Project_Path$/carafe
python setup.py develop
# or "pip install -v -e ."
cd ..
python ./carafe/grad_check.py