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	Inf time (fps)	Box AP	Mask AP	Download
Faster R-CNN w/ CARAFE	R-50-FPN	pytorch	1x	1000	16.5	38.6	38.6	model | log
-	-	-	-	2000
Mask R-CNN w/ CARAFE	R-50-FPN	pytorch	1x	1000	14.0	39.3	35.8	model | log
-	-	-	-	2000

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 your own 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