// the find
AlexeyAB/Yolo_mark
GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2
Yolo_mark is a desktop GUI for drawing bounding boxes on images to produce training data for YOLO v2/v3 (darknet). It is a C++/OpenCV application — compile it yourself, point it at a directory of JPEGs, draw boxes, get annotation files. For anyone who needs to hand-label images for object detection and wants something lightweight without a web browser.
The workflow is genuinely minimal: drop JPEGs in a folder, run the tool, get YOLO-format .txt files out with no intermediate conversion step. Video frame extraction is built in, which saves the usual ffmpeg scripting before labeling. The keyboard shortcuts are well-chosen — number keys for class switching, arrow keys to step through images — and faster than anything click-driven. OpenCV 2.x and 3.x compatibility means it runs on older systems without upgrading your stack.
Last commit was December 2020; the project is effectively abandoned. The entire repo is a single 1700-line main.cpp with no tests, no abstraction, and no error handling to speak of — any edge case in your image set will probably just crash it silently. Setup requires manually editing .sln files and hardcoding OpenCV paths on Windows, which is a real barrier. Modern alternatives (Label Studio, CVAT, Roboflow) support YOLO export and are actively maintained, run in a browser, and handle multi-user workflows — there is no reason to start a new project here unless you specifically need an offline C++ binary.