COCO Annotator vs YoloLabel

COCO Annotator is a web-based tool for creating image annotations used in training data for localization and object detection. It supports bounding boxes, polygons, segmentation masks, keypoints, and points, along with disconnected shapes treated as one instance, multiple labels on an image segment, and custom metadata. Users can import existing COCO datasets and export annotations in COCO format. Assisted options include DEXTR, MaskRCNN, Magic Wand, semi-trained model annotation, and Google Images dataset generation. The software includes user authentication and a REST API, with a Swagger interface at localhost:5000/api. It is self-hosted on Linux or Windows, and Docker with docker-compose is required because Docker is the only supported installation method. Documentation covers development and production builds, centralized datasets, and external access. The server uses Flask, Eventlet, and Gunicorn, while long-running requests go to RabbitMQ workers. The project recommends HTTPS to encrypt browser-to-site communication and invites users to its Discord community. It is listed as free under an MIT-licensed plan.

YoloLabel is a free GUI for drawing object bounding boxes on images used to train YOLO neural networks. It loads JPG or PNG files from a directory and creates boxes with a two-click method. Labels can be moved, resized, copied, pasted, undone, and redone, then exported as YOLO TXT. The desktop app has downloads for Windows x64, Linux x64, and macOS on Apple Silicon, and its repository uses the MIT License. Local assisted labeling runs Ultralytics models exported to ONNX, including YOLOv5, YOLOv8, YOLO11, YOLO12, and YOLOv26, with single-image or dataset batch processing. YoloLabel AI adds cloud open-vocabulary detection through an API key and optional prompt; requests handle up to 20 images. Building from source uses Qt 6, while ONNX Runtime is optional unless local auto-labeling is required.