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How ,Mask,-,RCNN, works? ,Mask,-,RCNN, is a result of a series of improvements over the original ,R-CNN paper, (by R. Girshick et. al., CVPR 2014) for object detection. ,R-CNN, generated region proposals based on selective search and then processed each proposed region, one at time, using Convolutional Networks to output an object label and its bounding box.
It breaks the instance segmentation process into two parts i.e. it generates a set of prototype ,masks, in parallel with predicting per-instance ,mask, coefficients. Then the prototypes are linearly combined with the ,mask, coefficients to produce the instance ,masks,. References & Further Readings: ,Mask R-CNN paper,; Faster ,R-CNN paper,; FPN ,paper,; MS R ...
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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.
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The weights are available from the project GitHub project and the file is about 250 megabytes. Download the model weights to a file with the name ‘,mask,_,rcnn,_coco.h5‘ in your current working directory. Download Weights (,mask,_,rcnn,_coco.h5) (246 megabytes) Step 2. Download Sample Photograph. We also need a photograph in which to detect objects.
Mask,-,RCNN, decouples these tasks: the existing bounding-box prediction (AKA the localization task) head predicts the class, like faster-,RCNN,, and the ,mask, branch generates a ,mask, for each class, without competition among classes (e.g. if you have 21 classes the ,mask, branch predicts 21 ,masks, instead of FCN's single ,mask, with 21 channels).