COCO Annotator vs Segments.ai
COCO Annotator
github.comSegments.ai
segments.aiSide by side
| Figure | COCO Annotator | Segments.ai |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 2 | 4 |
| Spec score · AI Image Annotation Tools | 9.0 (#2) | 8.1 (#9) |
| Model-assisted labeling | Images can be sent to semi-trained models, including built-in Mask R-CNN and DEXTR integrations, to generate annotations. | Users can upload model predictions as pre-labels, verify them and correct errors inside the platform. |
| Support | The project invites users to join its Discord community of machine-learning practitioners. | Fusion includes priority support and mail, Slack, and Discord support. |
| What it does | COCO Annotator is a web application for creating image-training annotations for object detection and localization. | Segments.ai is a cloud data-labeling platform for computer-vision, robotics and autonomous-vehicle teams, supporting images, videos, point clouds and multi-sensor data. |
Plans
Segments.ai
- Academic licenseFree
- CorePrice not published
- EnterprisePrice not published
- FusionPrice not published
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.
Segments.ai
Segments.ai is a data labeling platform for robotics and autonomous vehicle work, with tools for both images and 3D point clouds. Teams can mark data with segmentation, cuboids, bounding boxes, keypoints, polygons, and polylines, view images alongside point clouds, and project labels between sensors. For sequences, Batch Mode, merged point clouds, interpolation, and automated tracking support labeling across objects and frames. Image workflows include Superpixel, Autosegment, hover-and-click segmentation, and model predictions that teams can review and correct. Its Python SDK handles uploads, label downloads, dataset management, and webhooks; TensorFlow and PyTorch are named integrations. The product is available through web and API access, and datasets can be exported as JSON, PyTorch, TensorFlow, or Hugging Face formats. Core includes cloud bucket integrations. The company states it is EU based, ISO 27001 certified, and GDPR compliant. Pricing is on request, with a 14-day trial; a free academic license is available for non-commercial research at free.