Coco Annotator: Image Segmentation and Keypoint Labeling Framework

Image segmentation, object detection, and keypoint labels are generated through this web-based framework created by jsbroks and last updated in January 2025. It facilitates the creation of structured computer vision datasets in the COCO format from user-provided image files.

Use Cases

• Training Mask R-CNN models using exported segmentation masks
• Developing pose estimation algorithms with keypoint labels
• Fine-tuning object detectors with COCO-formatted bounding boxes

Strengths

• MIT licensed for open-source modification
• Produces COCO standard JSON output
• Supports three distinct annotation types: bounding boxes, polygons, and keypoints

Limitations

• Does not include a pre-existing image corpus
• Requires local or server-side deployment to access data generation features

Provenance

Source

jsbroks GitHub repository

Collection Method

annotated

Freshness

Updated as recently as January 2025.