October 8, 2019

396 words 2 mins read



Codes for paper “Mask Scoring R-CNN”.

repo name zjhuang22/maskscoring_rcnn
repo link https://github.com/zjhuang22/maskscoring_rcnn
language Python
size (curr.) 5337 kB
stars (curr.) 1672
created 2019-02-27
license MIT License

Mask Scoring R-CNN (MS R-CNN)

By Zhaojin Huang, Lichao Huang, Yongchao Gong, Chang Huang, Xinggang Wang.

CVPR 2019 Oral Paper, pdf

This project is based on maskrcnn-benchmark.


Mask Scoring R-CNN contains a network block to learn the quality of the predicted instance masks. The proposed network block takes the instance feature and the corresponding predicted mask together to regress the mask IoU. The mask scoring strategy calibrates the misalignment between mask quality and mask score, and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation. By extensive evaluations on the COCO dataset, Mask Scoring R-CNN brings consistent and noticeable gain with different models and different frameworks. The network of MS R-CNN is as follows:

alt text


Check INSTALL.md for installation instructions.

Prepare Data

  mkdir -p datasets/coco
  ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
  ln -s /path_to_coco_dataset/train2014 datasets/coco/train2014
  ln -s /path_to_coco_dataset/test2014 datasets/coco/test2014
  ln -s /path_to_coco_dataset/val2014 datasets/coco/val2014

Pretrained Models

  mkdir pretrained_models
  #The pretrained models will be downloaded when running the program.

My training log and pre-trained models can be found here link or link(pw:xm3f).


Single GPU Training

  python tools/train_net.py --config-file "configs/e2e_ms_rcnn_R_50_FPN_1x.yaml" SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 720000 SOLVER.STEPS "(480000, 640000)" TEST.IMS_PER_BATCH 1

Multi-GPU Training

  export NGPUS=8
  python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "configs/e2e_ms_rcnn_R_50_FPN_1x.yaml" 


NetWork Method mAP(mask) mAP(det)
ResNet-50 FPN Mask R-CNN 34.2 37.8
ResNet-50 FPN MS R-CNN 35.6 37.9
ResNet-101 FPN Mask R-CNN 36.1 40.1
ResNet-101 FPN MS R-CNN 37.4 40.1


alt text The left four images show good detection results with high classification scores but low mask quality. Our method aims at solving this problem. The rightmost image shows the case of a good mask with a high classification score. Our method will retrain the high score. As can be seen, scores predicted by our model can better interpret the actual mask quality.


The work was done during an internship at Horizon Robotics.


If you find MS R-CNN useful in your research, please consider citing:

    author = {Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang},
    title = {{Mask Scoring R-CNN}},
    booktitle = {CVPR},
    year = {2019},


maskscoring_rcnn is released under the MIT license. See LICENSE for additional details.

Thanks to the Third Party Libs


comments powered by Disqus