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SPECIFICTION
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COCO Annotator vs Scale Rapid

COCO Annotator

github.com

Scale Rapid

scale.com

Side by side

FigureCOCO AnnotatorScale Rapid
Price fromFree—
Free planYes—
Free trial——
Open sourceYes—
WebYesYes
WindowsYes—
LinuxYes—
APIYesYes
Self-hostedYes—
Plans published21
Spec score · AI Image Annotation Tools9.0 (#2)7.4 (#19)
PurposeCOCO Annotator is a web-based image annotation tool for creating training data for image localization and object detection.Scale Rapid provides machine learning engineers and researchers with high-quality labels and instruction feedback, in as little as one hour.

Plans

COCO Annotator

  • MIT-licensed softwareFree
  • Open-source self-hostedFree

COCO Annotator pricing →

Scale Rapid

  • Scale Rapid pay-as-you-goPriced on request

Scale Rapid pricing →

In their own words

COCO Annotator

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.

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Scale Rapid

Scale Rapid provides machine learning engineers and researchers with labeled data and instruction feedback for experiments, with availability described as being possible in as little as one hour. Customers can create labeling projects, write and submit their own instructions, and upload data through the web interface or API. They can improve quality by creating or updating evaluation tasks, then inspect quality and throughput metrics, including edge case detection. Rapid supports bounding box, polygon, line, cuboid, ellipse, and point annotations, and includes a review workflow. Research teams and startups can use it while iterating on experimental models and labeling instructions. Scale describes Rapid as early access and directs interested customers to a waitlist or contact process. The paid, pay-as-you-go plan is billed per label, with no minimum commitments, annual contracts, or platform fees. Listed platforms are web and API. Scale documentation includes product guides, workflows, API concepts, and endpoint references.

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