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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.
10/6/2019, · ,mask,_,rcnn,_coco.h5 : Our pre-trained ,Mask R-CNN, model weights file which will be loaded from disk. maskrcnn_predict.py : The ,Mask R-CNN, demo script loads the labels and model/weights. From there, an inference is made on a testing image provided via a command line argument.
Mask R-CNN,: ,Mask R-CNN, adopts the same two-stage procedure, with an identical first stage (which is RPN). In the second stage, in ,parallel, to predicting the class and box offset, ,Mask R-CNN, also outputs a binary ,mask, for each RoI. This is in contrast to most recent systems, where classification depends on ,mask, predictions (e.g. [33, 10, 26]).
20/3/2017, · Our method, called ,Mask R-CNN,, extends Faster ,R-CNN,  by adding a branch for predicting segmentation ,masks, on each Region of Interest (RoI), in ,parallel, with the existing branch for classification and bounding box regression (Figure 1).The ,mask, branch is a small FCN applied to each RoI, predicting a segmentation ,mask, in a pixel-to-pixel manner.
20/3/2017, · 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.
Mask R-CNN (He et al., ICCV 2017) is an improvement over Faster RCNN by including a mask predicting branch parallel to the class label and bounding box prediction branch as shown in the image below. It adds only a small overhead to the Faster R-CNN network and hence can still run at 5 fps on a GPU.
Mask R-CNN takes the same two-stage procedure that Faster R-CNN takes. The first stage which is identical, is RPN (Region Proposal Network). The second stage, in parallel to predicting the class and box offset, also outputs the binary mask for each region of interest.