computer vision

CNNs are foundational even as transformers rise, and this checks whether you understand why convolution suits images. The signal is parameter sharing and local connectivity, what pooling buys, and how the receptive field grows. Here is the answer.

Boxes and labels, not one tag per image. The signal is choosing two-stage vs one-stage by your latency and accuracy budget, knowing what NMS and anchors actually do, and reporting mAP correctly. Here is the answer interviewers score highest.

Semantic labels every pixel by class; instance separates each object. The signal is knowing that one cannot count overlapping objects, why Mask R-CNN adds a mask branch on RoIAlign, and what RoIAlign fixed. Here is the answer.

Detecting and boxing objects at scale comes down to one driving tradeoff plus a handful of CV specifics most candidates fumble: the detector family, NMS, focal loss, and why mAP not accuracy is the metric. Here is the design.