You can find the complete code for this example on my Github. We'll start by looking at how to load in a pre-trained model and use it to perform inference. You must have the Visual C++ 2015 build tools on your path (see the repo for additional details) They are forks of the original pycocotools with fixes for Python3 and Windows (the official repo doesn't seem to be active anymore). (Optional) To train or test on MS COCO install pycocotools from one of these repos. ![]() Run setup.py script python3 setup.py installĤ. For this, you'll have to work through the following steps:ģ. To get started, you'll have to install Mask R-CNN on your machine. Figure 1: The Mask R-CNN framework, for instance segmentation ( Source) Matterport Mask R-CNN Installation ![]() 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 an extension of Faster R-CNN, a popular object detection algorithm. In this article, I'll go over what Mask R-CNN is, how to use it in Keras to perform object detection and instance segmentation, and how to train a custom model.
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