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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