RectLabel

Categories: Coding & Developer Tools, Data Analysis, Automation / Agents | Pricing: Pricing unavailable | Official Website ↗

RectLabel is an offline image annotation tool leveraging AI models for object detection and segmentation, streamlining the data labeling process.

RectLabel is an offline image annotation tool designed for object detection and segmentation tasks. It integrates various AI models like Segment Anything Model, Cellpose, and YOLO26 to automate and accelerate the labeling of bounding boxes, polygons, pixels, and keypoints. The tool supports exporting data in popular formats like YOLO, COCO, and CreateML for training custom machine learning models.

Key Features

Automatic labeling with Segment Anything Model 3
Polygon and pixel labeling with SAM 2 & Cellpose
Bounding box labeling with Tracking model
Automatic labeling with Core ML models (RF-DETR, YOLO26)
Text recognition for lines and words
Export to YOLO, COCO, CreateML, DOTA formats
Video to image frame conversion

Pros

Utilizes advanced AI models for automatic labeling
Supports various annotation types (boxes, polygons, pixels, keypoints)
Offline functionality for privacy and speed
Exports to popular ML formats (YOLO, COCO)
GPU acceleration for Segment Anything Model

Cons

Requires manual setup for some AI models (CUDA, cuDNN)
No mention of cloud-based collaboration features
Specific to image annotation, not general data labeling
Pricing details not immediately visible on the main page
May have a learning curve for new users

Use Cases

Preparing datasets for object detection models
Creating segmentation masks for instance segmentation
Annotating images for custom AI model training
Labeling keypoints for pose estimation
Automating repetitive annotation tasks

Best For

Machine learning engineers
Data scientists
Computer vision researchers
AI model developers

Platforms: mac, linux

Watch demo on YouTube ↗

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