Mask R-CNN is an advanced deep learning model designed for object instance segmentation, built upon the Faster R-CNN framework to add a branch for predicting segmentation masks on each Region of Interest (RoI). It excels in simultaneously performing object detection, classification, and precise pixel-level segmentation, making it highly effective in tasks requiring detailed scene understanding.

Mask R-CNN

Deep learning model

Object instance segmentation

Faster R-CNN framework

Segmentation masks

A Region of Interest (ROI) is a specified subset within a dataset or image that is selected for focused analysis or processing, allowing for more efficient and targeted data handling. ROIs are crucial in various fields such as medical imaging, remote sensing, and computer vision, where they help in isolating relevant information from large datasets, enhancing analytical precision and reducing computational load.

Region Of Interest

Object detection is a computer vision task that involves identifying and locating objects within an image or video. It combines classification and localization to not only recognize what objects are present but also determine their positions in the visual data.

Object Detection

Classification is a supervised learning approach in machine learning where the goal is to predict the categorical label of a given input based on training data. It is widely used in applications such as spam detection, image recognition, and medical diagnosis, where the output is discrete and predefined.

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