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3/1/2020, · ,Mask R,-,CNN, architecture:,Mask R,-,CNN, was proposed by Kaiming He et al. in 2017.It is very similar to Faster ,R,-,CNN, except there is another layer to predict segmented. The stage of region proposal generation is same in both the architecture the second stage which works in parallel predict class, generate bounding box as well as outputs a binary ,mask, for each RoI.
10/2/2020, · In this tutorial, you’ll learn how to use OpenCV’s “dnn” module with an NVIDIA GPU for up to 1,549% faster object detection (,YOLO, and SSD) and instance segmentation (,Mask R,-,CNN,).. Last week, we discovered how to configure and install OpenCV and its “deep neural network” (dnn) module for inference using an NVIDIA GPU.. Using OpenCV’s GPU-optimized dnn module we were able to push a ...
Mask R,-,CNN,: Extension of Faster ,R,-,CNN, that adds an output model for predicting a ,mask, for each detected object. The ,Mask R,-,CNN, model introduced in the 2018 paper titled “ ,Mask R,-,CNN, ” is the most recent variation of the family models and supports both object detection and object segmentation.
In this work, they used the ,Mask R,-,CNN, to detect the number of people. On the same hand, the Faster ,R,-,CNN,  is extended to ,Mask R,-,CNN, by adding a branch to predict segmentation ,masks, for each Region of Interest (RoI) generated in Faster ,R,-,CNN,. In the end, the authors measured the model in terms of Precision and Recall over the image sequences.
Segnet ,vs Mask R,-,CNN, Segnet - Dilated convolutions are very expensive, even on modern GPUs. - ,Mask R,-,CNN, - Without tricks, ,Mask R,-,CNN, outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. - Better for pose detection
Multinomial ,vs,. Independent ,Masks,. Replace softmax with sigmoid. ,Mask R,-,CNN, decouples ,mask, and class prediction: as the existing box branch predicts the class label, we generate a ,mask, for each class without competition among classes (by a per-pixel sigmoid and a binary loss).
The ,Mask R,-,CNN, framework is built on top of Faster ,R,-,CNN,. So, for a given image, ,Mask R,-,CNN,, in addition to the class label and bounding box coordinates for each object, will also return the object ,mask,. Let’s first quickly understand how Faster ,R,-,CNN, works. This will help us grasp the intuition behind ,Mask R,-,CNN, …
Faster ,R,-,CNN vs,. ,Mask R,-,CNN, performance. We know the ,Mask R,-,CNN, is computationally more expensive than Faster ,R,-,CNN, because ,Mask R,-,CNN, is based on Faster ,R,-,CNN,, and it does the extra work for generating the ,mask,. How much more expensive? Let’s find out. 3.1 Comparing the inference time of model in CPU & GPU.