README.md

Mask R-CNN

Mask R-CNN

Abstract

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition.

Results and models

CTW1500

Method	BackBone	Pretrained Model	Training set	Test set	#epochs	Test size	Precision	Recall	Hmean	Download
MaskRCNN	-	-	CTW1500 Train	CTW1500 Test	160	1600	0.7165	0.7776	0.7458	model | log
MaskRCNN_r50-oclip	ResNet50-oCLIP	-	CTW1500 Train	CTW1500 Test	160	1600	0.753	0.7593	0.7562	model | log

ICDAR2015

Method	BackBone	Pretrained Model	Training set	Test set	#epochs	Test size	Precision	Recall	Hmean	Download
MaskRCNN	ResNet50	-	ICDAR2015 Train	ICDAR2015 Test	160	1920	0.8644	0.7766	0.8182	model | log
MaskRCNN_r50-oclip	ResNet50-oCLIP	-	ICDAR2015 Train	ICDAR2015 Test	160	1920	0.8695	0.8339	0.8513	model | log

Citation

@INPROCEEDINGS{8237584,
  author={K. {He} and G. {Gkioxari} and P. {Dollár} and R. {Girshick}},
  booktitle={2017 IEEE International Conference on Computer Vision (ICCV)},
  title={Mask R-CNN},
  year={2017},
  pages={2980-2988},
  doi={10.1109/ICCV.2017.322}}