COCO Annotator

COCO Annotator is a self-hosted web application for preparing image data for localization and object-detection training. It is a strong fit for teams that need flexible annotations and COCO-format exchange, provided they can operate a Docker-based deployment. Its breadth is useful; its installation requirement is the deciding constraint.

Overview

The tool supports the central dataset workflow: annotate images, import datasets already in COCO format, and export COCO JSON. That makes it particularly relevant when compatibility with COCO datasets is central to a project. It is less suitable for individuals looking for a conventional desktop application or a deployment that avoids Docker.

Key features

Annotation and assisted labeling

Annotations include bounding boxes, polygons, segmentation masks, keypoints and points. Users can assign multiple labels to an image segment, group disconnected objects as one instance, and attach custom metadata. This range accommodates more than basic object boxes, though it is oriented around image annotation rather than a broader data-labeling workflow.

DEXTR, MaskRCNN, Magic Wand and semi-trained model annotation provide assisted labeling options; Google Images dataset generation is also included. These capabilities can help teams combine different annotation approaches, while the COCO import and export workflow keeps the result aligned with a widely used dataset format.

API and deployment

The resource-oriented REST API uses HTTP response codes and mostly JSON responses. A Swagger interface is available at localhost:5000/api, and the feature set includes user authentication. This gives technical teams an interface for integrating with the application, but users who do not need an API should not mistake its presence for a simpler setup.

Docker and docker-compose are required: Docker is the only supported installation method. Production and development Docker builds are documented, and the production build is described as stable and suitable for a large user base. Dedicated-server guidance covers centralized datasets and external access for outsourcing, with a recommended basic instance of 2GB RAM and 2 CPU cores. HTTPS is strongly recommended to encrypt browser-to-site communication.

The web server uses Flask, Eventlet and Gunicorn; RabbitMQ routes long-running requests to workers. Docker volumes store database-generated data and are described as compatible with Linux and Windows containers. These are meaningful deployment details for operators, but the operational footprint makes COCO Annotator a poor fit for someone who wants a plug-and-play service.

Pricing

COCO Annotator is free under the MIT-licensed software plan: free (self-hosted · Docker required). There are no paid tiers or seat quotas in the plan information. The trade-off is operational rather than financial: users must provide and manage a compatible Docker deployment.

Platforms

COCO Annotator is available as a web application and supports API use, self-hosting, Linux and Windows. Docker is required for installation, so platform support should be understood in the context of running its containerized deployment rather than installing a standalone desktop app.

Who it's for

Choose COCO Annotator if you need a free, self-hosted image annotation tool with multiple annotation types, assisted labeling, API access and direct COCO-format import and export—and have the capacity to manage Docker services. It is a less convincing choice for teams that want a hosted service, a non-Docker installation, or a labeling workflow outside image localization and object detection. The project invites users to join its Discord community of machine-learning practitioners for support.

Pros and cons

• Pro: COCO import and COCO JSON export support a focused workflow for teams building or extending COCO-format datasets.
• Pro: Boxes, polygons, masks, keypoints, points, custom metadata and assisted labeling cover a broad range of image-annotation needs.
• Pro: Free MIT-licensed software avoids subscription charges for self-hosted use.
• Con: Docker and docker-compose are mandatory, excluding users who cannot or do not want to operate containerized software.
• Con: The GitHub repository reports no detected SECURITY.md policy and no published security advisories; organizations may need to assess their own security requirements before deployment.

Alternatives

For a broader shortlist, see AI Image Annotation Tools.

• Label Studio is worth considering if you want a freemium tool with a free plan and free trial, and need platform support that includes macOS.
• Roboflow may suit teams seeking a freemium platform with a free tier that includes 10 credits a month; its Core plan costs $39 a month (billed billed mo).
• Amazon SageMaker Autopilot is a paid service with a free trial and pay-as-you-go pricing, a different fit for users seeking that pricing model rather than free self-hosted software.
• CVAT is another freemium option; its Community plan is free for personal use and small teams, while CVAT Online Free is limited to 1 member, 1 project, 3 tasks and 1 GB.
• Supervisely offers a free Community plan with 5 GB storage, 10,000 files and 2 members, plus unlimited projects and annotations; its Pro plan starts at €199 a month.
• Pixano is a free alternative with API, desktop operating-system and self-hosted platform support.
• YoloLabel is a free alternative with API, desktop operating-system and self-hosted platform support.
• Labelbox is a freemium alternative with a free plan.

Verdict

COCO Annotator is a sensible choice for technically capable teams building image-localization or object-detection datasets that need varied annotation tools and direct COCO-format handling without a software fee. Its free, self-hosted model is appealing, but Docker is non-negotiable and deployment remains the user's responsibility. Look elsewhere if you need a hosted or desktop-first tool, or cannot manage Docker services.