COCO Annotator

COCO Annotator is a self-hosted web tool for preparing image data used in image localization and object detection. It supports bounding boxes, polygons, segmentation masks, keypoints, and points, plus disconnected objects grouped as one instance, multiple labels on a segment, and custom metadata. The tool imports datasets in COCO format and exports annotations to COCO JSON. Assisted options include DEXTR, MaskRCNN, Magic Wand, semi-trained model annotation, and Google Images dataset generation. It also includes user authentication and a REST API, with a Swagger interface documented at localhost:5000/api. Docker and docker-compose are required because Docker is the only supported installation method. Deployment documentation covers production and development builds, centralized datasets, and external access; the guidance recommends a basic server with 2GB RAM and 2 CPU cores. The server uses Flask, Eventlet, and Gunicorn, and sends long-running requests to RabbitMQ workers. The project recommends HTTPS to encrypt browser-to-site communication. The listed MIT-licensed software plan is free.

COCO Annotator offers a broad set of annotation types and assisted tools for COCO-oriented datasets. Its Docker-only installation requirement is an important fit consideration.

COCO Annotator suits teams creating image datasets for localization or object detection, particularly those able to run a Docker-based service. Its centralized dataset guidance may be relevant to teams arranging external annotation access.

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. Its broad annotation toolkit and programmable API are compelling, but the Docker-only installation requirement is a serious constraint for anyone wanting a conventional app install.

Overview

The workflow centers on COCO data: import datasets that already use COCO annotations, label new images, and export the result as COCO JSON. Annotation types cover bounding boxes, polygons, segmentation masks, keypoints and points. You can assign multiple labels to one image segment, treat disconnected objects as a single instance, and attach custom metadata. That combination is well suited to varied computer-vision projects, especially when a team needs more than rectangular boxes while keeping one established exchange format.

It is a browser-based service rather than a desktop utility. Docker and docker-compose are required because they are the only supported installation method, so organizations without container operations experience should account for that work before choosing it.

Key features

Annotation and assisted labeling

DEXTR, MaskRCNN, Magic Wand and semi-trained-model annotation add assistance to manual labeling. These options can reduce repetitive work or provide useful starting points for complex shapes, while the underlying support for masks, keypoints and points handles projects that simple detection tools cannot. Google Images dataset generation offers another way to assemble source images. The practical advantage is breadth; the trade-off is that the feature set is most valuable to teams already building a COCO-oriented pipeline.

API and administration

The resource-oriented REST API uses standard HTTP response codes and mostly JSON responses. A Swagger interface is exposed at localhost:5000/api, making the endpoints easier to inspect and integrate, and a user authentication system is included. This is a credible foundation for scripted dataset operations and controlled access rather than a tool limited to point-and-click labeling.

Deployment and scaling

Documentation provides development and production Docker builds, describing the production build as stable and suitable for a large user base. Dedicated-server guidance supports centralized datasets and external access for outsourced annotation, with a recommended starting instance of 2GB RAM and 2 CPU cores. The architecture combines Flask, Eventlet and Gunicorn, while long-running requests are passed to workers through RabbitMQ. Docker volumes hold database-generated data and are described as compatible with Linux and Windows containers.

HTTPS is strongly recommended to encrypt browser-to-site communication. The project also invites users to a Discord community of machine-learning practitioners. Its repository reports no detected SECURITY.md policy and no published security advisories, so teams with formal security requirements should perform their own review and deployment hardening.

Pricing

MIT-licensed software: free (self-hosted; Docker required). There are no paid tiers or hosted-service quotas in this plan. The benefit is unrestricted control over deployment and data location; the cost is that your organization supplies the server, container setup, maintenance and secure network configuration. This is the right pricing model for developers and teams that can operate infrastructure, not for buyers seeking vendor-managed hosting or a support-backed subscription.

Platforms

COCO Annotator is offered through a web interface and API, with self-hosted deployment on Linux or Windows environments using Docker. It is therefore accessible from a browser while remaining an infrastructure-managed service, rather than a native desktop application with a standalone installer.

Who it's for

Choose it for machine-learning teams producing image-localization or object-detection data that must import and export COCO annotations, need several geometry types, and want assisted labeling or API automation. It also fits organizations that need centralized datasets for distributed or outsourced annotation and can run Docker services. It is a poor match for individuals who want a quick local install, teams that require hosted operations, or buyers unwilling to take responsibility for HTTPS, authentication configuration and server upkeep.

Pros and cons

• Pros: COCO import and COCO JSON export keep the workflow aligned with a widely used dataset format.
• Pros: Boxes, polygons, masks, keypoints, points, custom metadata and disconnected-object grouping cover demanding labeling schemas.
• Pros: DEXTR, MaskRCNN, Magic Wand, semi-trained annotation and Google Images generation extend the workflow beyond manual drawing.
• Pros: REST endpoints, Swagger documentation and authentication support integration and controlled multi-user access.
• Cons: Docker and docker-compose are mandatory, excluding conventional desktop installation and adding operational overhead.
• Cons: Self-hosting leaves infrastructure, HTTPS protection and security review to the deploying team.
• Cons: The repository reports no SECURITY.md policy or published advisories, which may matter to organizations with formal security-governance requirements.

Alternatives

For a wider shortlist, see AI Image Annotation Tools.

• Roboflow is the better choice when you want a freemium service spanning web, mobile, API and self-hosting, with a Free Tier that includes 10 credits per month.
• CVAT suits personal users and small teams seeking a free MIT-licensed community plan, or a constrained hosted free tier.
• Amazon SageMaker Autopilot is for teams preferring pay-as-you-go cloud model-building through an API or web interface rather than a dedicated annotation server.
• Label Studio is an alternative for users who want a freemium, self-hosted or hosted labeling platform across major desktop operating systems.
• Segments.ai is aimed at buyers considering a paid platform with a free academic license for non-commercial research.
• Supervisely offers a freemium hosted/self-hosted route, including a Community plan with 5 GB storage, 10,000 files and two members.
• VGG Image Annotator is a free BSD-licensed option with no paid tiers or stated usage limits.
• MakeSense.ai is a free browser-based photo-labeling tool for users who want a simpler, cross-platform starting point.

Verdict

COCO Annotator is a strong recommendation for technically capable teams that need rich annotation types, assisted labeling and dependable COCO-format exchange under their own control. Its decisive drawback is the Docker-only, self-managed deployment: choose another tool if you need hosted convenience, a native installer or vendor-managed security and operations.