mmdetection/configs/mask_rcnn

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

Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	box AP	mask AP	Config	Download
R-50-FPN	caffe	1x	4.3		38.0	34.4	config	model | log
R-50-FPN	pytorch	1x	4.4	16.1	38.2	34.7	config	model | log
R-50-FPN (FP16)	pytorch	1x	3.6	24.1	38.1	34.7	config	model | log
R-50-FPN	pytorch	2x	-	-	39.2	35.4	config	model | log
R-101-FPN	caffe	1x			40.4	36.4	config	model | log
R-101-FPN	pytorch	1x	6.4	13.5	40.0	36.1	config	model | log
R-101-FPN	pytorch	2x	-	-	40.8	36.6	config	model | log
X-101-32x4d-FPN	pytorch	1x	7.6	11.3	41.9	37.5	config	model | log
X-101-32x4d-FPN	pytorch	2x	-	-	42.2	37.8	config	model | log
X-101-64x4d-FPN	pytorch	1x	10.7	8.0	42.8	38.4	config	model | log
X-101-64x4d-FPN	pytorch	2x	-	-	42.7	38.1	config	model | log
X-101-32x8d-FPN	pytorch	1x	10.6	-	42.8	38.3	config	model | log

Pre-trained Models

We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.

Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	box AP	mask AP	Config	Download
R-50-FPN	caffe	2x	4.3		40.3	36.5	config	model | log
R-50-FPN	caffe	3x	4.3		40.8	37.0	config	model | log
R-50-FPN	pytorch	3x	4.1		40.9	37.1	config	model | log
R-101-FPN	caffe	3x	5.9		42.9	38.5	config	model | log
R-101-FPN	pytorch	3x	6.1		42.7	38.5	config	model | log
x101-32x4d-FPN	pytorch	3x	7.3		43.6	39.0	config	model | log
X-101-32x8d-FPN	pytorch	1x	10.4		43.4	39.0	config	model | log
X-101-32x8d-FPN	pytorch	3x	10.3		44.3	39.5	config	model | log
X-101-64x4d-FPN	pytorch	3x	10.4		44.5	39.7	config	model | log

Citation

@article{He_2017,
   title={Mask R-CNN},
   journal={2017 IEEE International Conference on Computer Vision (ICCV)},
   publisher={IEEE},
   author={He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross},
   year={2017},
   month={Oct}
}