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Core
Object Detection and Segmentation
Detection finds objects as boxes plus labels; segmentation labels pixels (semantic) or per-object pixels (instance). The machinery is shared: a pretrained backbone feeds a head, anchors or queries propose objects, IoU measures box overlap, and NMS removes duplicates. Two-stage detectors (Faster R-CNN) trade speed for accuracy, one-stage (YOLO) flip it, and Mask R-CNN adds a mask branch for instance segmentation. Interviews probe this to see you pick by latency and accuracy and know what NMS, IoU, and anchors actually do.
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Machine Learning & Data ScienceHow would you build an object detection system that detects and localizes objects in images?→Machine Learning & Data ScienceExplain semantic vs instance segmentation and how Mask R-CNN works.→System Design for AI in ProductionDesign the perception system for an autonomous vehicle (or robot).→System Design for AI in ProductionDesign an object detection service (detect and localize objects in images at scale).→Machine Learning & Data ScienceHow do you choose loss functions for computer vision tasks (classification, detection, segmentation)?→
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