Object Detection — YOLO from Scratch
Detection is classification plus regression, run at every position in a feature map, then cleaned up with non-maximum suppression. Explain the grid-and-anchor design that turns detection into a dense prediction problem and state what every number in the output tensor means. Compute Intersection-over-Union between boxes and implement non-maximum suppression from scratch. Build a minimal YOLO-style head on top of a pretrained backbone, including the classification, objectness, and box-regression losses. Read a detection metric row (precision@0.5, recall, mAP@0.5, mAP@0.5:0.95) and pick which knob to turn next. Classification says "this image is a dog." Detection says "there is a dog at pixels (112, 40, 280, 210), there is a cat at (400, 180, 560, 310), and nothing else in the frame." That one structural change — predicting a variable number of labelled boxes instead of one label per image — is what every autonomous system, every surveillance product, every document layout parser, and every factory vision line depends on. Detection is also where every engineering trade-off in vision shows up at once. You want boxes that are accurate (regression head), you want the right class for each box (classification head), you want the model to know when there is nothing to detect (objectness score), and you want exactly one prediction per real object (non-maximum suppression). Miss any of these and the pipeline…
Object Detection — YOLO from Scratch: Detection is classification plus regression, run at every position in a feature map, then cleaned up with non-maximum…
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