RectLabel is an offline image annotation tool for object detection and segmentation.

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Name

Rank

Metrics

17,164

Language:: Python
Stars:: 553
Forks:: 132
Description:: [ECCV2020 Oral] Learning Lane Graph Representations for Motion Forecasting.

17,167

Language:: Python
Stars:: 552
Forks:: 94
Description:: Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation, CVPR 2020 (Oral).

17,170

17,172

Language:: Python
Stars:: 552
Forks:: 61
Description:: BANMo Building Animatable 3D Neural Models from Many Casual Videos.

17,182

17,185

Language:: C++
Stars:: 551
Forks:: 67
Description:: A high-fidelity, open-source robotics simulator integrating Unreal Engine's photorealistic rendering with MuJoCo's precision physics.

17,189

17,190

Language:: Python
Stars:: 551
Forks:: 52
Description:: 🦖Pytorch implementation of popular Attention Mechanisms, Vision Transformers, MLP-Like models and CNNs.

Key parameters and specifications
Parameter	Value
Language	Jupyter Notebook
Category	Model Development
Subcategory	Dataset engineering
Keywords	Computer vision, Data labeling
Stars	552
Forks	72
Contributors	1
Created date	2017-03-28
Updated date	2026-08-22
Stars in 1 day	0
Stars in 7 days	0
Rank	17,177
Previous rank	16,961
Rank change	-216
7-day stars rank	15,953
Previous 7-day stars rank	15,960
7-day stars rank change	7
Total stars rank	17,921
Previous total stars rank	17,894
Total stars rank change	-27
Country	Japan

Parameter

Value

Language

Jupyter Notebook

Rectlabel-support

This is the support page for RectLabel.

To download RectLabel apps.

How to use? Read our Help page.

Post feature requests to Issues page.

If you need to implement RectLabel-like app in your system, please contact us. support@rectlabel.com

Key features

Using text and box prompts of Segment Anything Model 3, multiple objects are labeled at once
Label polygons and pixels using Segment Anything Model 2
Label polygons and pixels using Cellpose model
Label bounding boxes using Tracking model
Automatic labeling using Core ML models including RF-DETR and YOLO26
Automatic text recognition for lines and words
Label cubic bezier curves, line segments, and points
Label oriented bounding boxes in aerial images
Label keypoints with a skeleton
Label pixels with brushes and superpixels
Settings for objects, attributes, hotkeys, and labeling fast
Search object, attribute, image names, and memo in a gallery view
Export to YOLO, COCO, CreateML, and DOTA formats
Export indexed color mask image and grayscale mask images
Video to image frames, augment images, etc.