Annotate Images for Object Detection

I have a set of images that must be meticulously labeled to build a high-quality object-detection training dataset. Each picture may contain between six and ten object categories, and every visible instance of those classes needs a tight, accurate bounding box with the correct class name.

Consistency and precision come first. Use the annotation platform you are most comfortable with—CVAT, Labelbox, or a comparable web-based tool are all fine—so long as the final labels arrive in COCO JSON (YOLO TXT is acceptable as an alternative) and reference the original image filenames unchanged.

Deliverables
• One archive containing the annotation files in COCO JSON or YOLO format
• A brief report outlining total image count, per-class object count, and any edge cases encountered

Acceptance criteria
• Bounding boxes snugly enclose each object without cutting edges
• No duplicate or overlapping boxes for the same instance
• Class names match the agreed six-to-ten item list exactly
• Spot-check accuracy on 50 random images must reach 98 % or higher

Once approved, I will share the labeling guidelines, class list, and image download link so you can start right away.