Principle:Ggml org Ggml Detection Post Processing

Template:PrincipleCard

Summary

Converting raw neural network detection outputs into usable bounding boxes with class labels.

Theory

YOLO-style detection post-processing involves multiple steps:

1. Sigmoid activation on raw predictions (objectness, class probabilities)
2. Anchor-based box decoding: convert grid offsets to absolute coordinates using anchor priors
3. Letterbox correction: map coordinates back from padded input space to original image space
4. Non-Maximum Suppression (NMS): remove duplicate detections by comparing IoU between boxes

NMS Algorithm

The Non-Maximum Suppression algorithm operates as follows:

1. Sort detections by confidence (highest first)
2. For each detection, suppress lower-confidence overlapping detections where IoU > threshold

This eliminates duplicate detections of the same object, keeping only the most confident prediction.

Multi-Scale Detection

Combine predictions from different grid resolutions to handle objects of varying sizes:

• 13x13 grid — for large objects
• 26x26 grid — for small objects

Multi-scale detection ensures robust coverage across the full range of object sizes present in an image.

Related

• Implementation:Ggml_org_Ggml_Yolo_detect