COCO Annotator vs Pixano
COCO Annotator
github.comPixano
pixano.cea.frSide by side
| Figure | COCO Annotator | Pixano |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 0 |
| Spec score · AI Image Annotation Tools | 9.0 (#2) | 7.6 (#15) |
| Deployment | The documentation provides production and development Docker builds, and describes the production build as stable and suitable for a large user base. | The maker documents installation with pip in a Python virtual environment and official Docker releases. |
| Installation | Docker and docker-compose are required because Docker is currently the only supported installation method. | The project README describes installation with pip in a Python virtual environment or by running an official Docker image. |
| License | The repository is distributed under the MIT License, including commercial use rights with attribution. | Pixano is licensed under CeCILL-C. |
| Purpose | COCO Annotator is a web-based image annotation tool for creating training data for image localization and object detection. | Pixano is an open-source tool for exploring and annotating computer vision datasets. |
| Support | The project invites users to join its Discord community of machine-learning practitioners. | The project README directs users to its Getting Started guide and contributing guide for usage and contribution information. |
Plans
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.
Pixano
Pixano is a free, open-source tool for exploring and annotating computer-vision datasets, including collections of text, images, and video. Its annotation tools cover boxes, polygons, pixel masks, keypoints, cuboids, classification, and tracking; video labels can be propagated across time. Smart features include semantic search using models such as CLIP and segmentation using models such as SAM. Dataset formats such as COCO can be imported and exported, and Pixano uses Lance storage for dataset navigation. Its reusable Web Components can be assembled into custom apps. Pixano documents REST and Python APIs, along with Pixano Inference, a Ray Serve-based inference server with a Python client and REST API. Installation options documented by the project are pip in a Python virtual environment or an official Docker image. The supported Python range is 3.10 or later and earlier than 3.14. Pixano is licensed under CeCILL-C and is under active development, so its API may change. Three-dimensional point-cloud support is described as planned.