COCO Annotator

COCO Annotator is a self-hosted web application for building image datasets used in localization and object detection. It suits teams comfortable operating Docker-based services who need flexible labels and COCO-compatible data handling. For those teams, its range of annotation tools and API are compelling; the installation constraint is substantial.

Overview

The workflow covers annotating images, importing datasets already in COCO format, and exporting annotations as COCO JSON. It supports bounding boxes, polygons, segmentation masks, keypoints and points, plus multiple labels per segment, disconnected objects grouped as one instance, and custom metadata. That range accommodates varied labeling tasks without straying from a COCO-centered workflow, but users seeking a locally installed desktop app should look elsewhere.

Key features

Annotation assistance

DEXTR, MaskRCNN, Magic Wand and semi-trained-model annotation provide assisted options alongside manual labeling. Google Images dataset generation can help assemble image data. This mix is useful when a project needs more than box drawing, though the strongest fit remains teams whose training data and exports revolve around image annotation in COCO format.

API and operations

The resource-oriented REST API uses HTTP response codes and mostly JSON responses, with a Swagger interface at localhost:5000/api. Authentication is included. The server stack uses Flask, Eventlet and Gunicorn, while RabbitMQ passes long-running requests to workers; these are relevant operational details for teams running the service, rather than a reason to choose it over a simpler hosted tool.

Docker and docker-compose are required: Docker is the only supported installation method. Production and development 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, recommending a basic instance with 2GB RAM and 2 CPU cores. HTTPS is strongly recommended to encrypt browser-to-site communication. Docker volumes store database-generated data and are described as compatible with Linux and Windows containers.

The project invites users to its Discord community of machine-learning practitioners. Its GitHub repository reports no detected SECURITY.md policy and no published security advisories; teams with formal security review requirements should factor that into deployment decisions.

Pricing

COCO Annotator is MIT-licensed software listed as free, self-hosted, with Docker required. There are no paid tiers or per-seat or quota terms in this plan. It is a strong fit for teams able to provide their own deployment and operations; the trade-off is that the free price does not remove the need to manage the service yourself.

Platforms

It is a web-based, self-hosted application with API access, and the platform listing includes Linux and Windows. Docker volumes are described as compatible with Linux and Windows containers. This is not a native desktop workflow: installation requires Docker and docker-compose.

Who it's for

Choose COCO Annotator if your team needs several annotation types, assisted labeling, COCO import and export, and an API in a self-hosted setup. It is less suitable for individuals or organizations that cannot operate Docker services, need a conventional desktop install, or require a clearly published security policy.

Pros and cons

• Pro: Broad support for boxes, polygons, masks, keypoints and points, plus metadata and multiple labels, covers more than basic object detection boxes.
• Pro: COCO import and export keep the workflow aligned with an established annotation format.
• Pro: Assisted tools and a REST API add options for labeling and integration.
• Con: Docker and docker-compose are mandatory, limiting adoption for teams without container operations.
• Con: The repository reports no security policy file and no published advisories, which leaves less formal security guidance to assess.

Alternatives

For a broader comparison, see AI Image Annotation Tools.

• Label Studio is worth considering if a freemium option with a free trial and support for macOS as well as web, API, Linux, self-hosted and Windows platforms better fits your setup.
• Roboflow offers a free tier with 10 credits monthly, described as enough to train about 30 models or run 80,000 inferences; its Core plan costs $39 a month. Consider it if that hosted-service credit model suits your workload better than self-hosting.
• CVAT has a free MIT-licensed Community plan for personal use and small teams, alongside a free online plan capped at 1 member, 1 project, 3 tasks and 1 GB. Choose it if those limits fit your use case.
• Amazon SageMaker Autopilot is a paid product with no free plan and a free trial; its pay-as-you-go plan has no minimum fees or upfront commitments. It may fit readers who prefer that usage-based payment model.
• Supervisely has a free Community plan with 5 GB storage, 10,000 files and 2 members, with unlimited projects and annotations. Its Pro plan starts at €199 a month. Consider it if those hosted plan terms suit your team.
• Pixano is a free option available across API, Linux, macOS, self-hosted, web and Windows platforms.
• YoloLabel is a free option for API, Linux, macOS, self-hosted and Windows platforms.
• Prodigy is a paid option with a free trial and support for API, Linux, macOS, self-hosted, web and Windows.

Verdict

COCO Annotator is a good choice for teams that need varied image annotations, COCO data handling and assisted tools under their own control. Its free, MIT-licensed plan keeps software cost at zero, but Docker-only installation and the need to run a service make it a poor fit for those seeking a ready-hosted or desktop tool. Choose it when self-hosting is an acceptable cost of getting a broad COCO-focused workflow.