object detection
Applied AI interview questions tagged object detection, across every topic.
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Concepts behind "object detection"
The curriculum that explains the ideas these questions test.
Core
CV Architectures: ResNets, ViT, DetectionModern computer vision rests on three pillars: residual connections that let CNNs go hundreds of layers deep without degrading, Vision Transformers that patchify an image and run self-attention instead of convolutions, and detection heads (one-stage vs two-stage) scored by mAP after non-maximum suppression. Applied AI interviews probe this to check you can pick an architecture, fine-tune a pretrained backbone, and reason about latency vs accuracy rather than train from scratch.📊 Evaluation & ML FoundationsSign in
Core
Object Detection and SegmentationDetection 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.📊 Evaluation & ML FoundationsSign in