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NLP data labeling platform with AI-assisted automation, quality workflows, and private LLM options
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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Intuitive Python tool for polygonal image and video annotation
Labelme provides a graphical interface for creating polygon, rectangle, circle, line, point, and image‑level flag annotations, supporting VOC/COCO export, video labeling, and customizable label sets.
Labelme is a Python‑based graphical annotation application built with Qt. It targets computer‑vision researchers, dataset curators, and developers who need precise polygonal, rectangular, circular, line, point, or image‑level flag annotations. The interface runs on Windows, macOS, and Linux, allowing users to label single images or entire directories with minimal setup.
The tool supports export to VOC and COCO formats, facilitating seamless integration with popular segmentation and detection pipelines. Video annotation is available directly in the GUI, and label sets can be predefined, auto‑saved, and validated to reduce errors. Installation is flexible: a simple pip install labelme, a downloadable standalone executable that bundles all dependencies, or native packages from major Linux distributions. Annotations are saved as JSON files, optionally omitting embedded image data for lightweight storage. Extensive examples and a command‑line interface further streamline batch processing and custom workflows.
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Semantic segmentation dataset creation
Generate VOC/COCO masks for training segmentation models
Instance segmentation labeling
Annotate object polygons to feed instance detection pipelines
Video frame labeling
Produce per‑frame annotations for action‑recognition datasets
Image classification flagging
Assign image‑level tags for dataset cleaning and filtering
Install via pip (`pip install labelme`), download the standalone executable, or use your Linux distribution’s package manager.
Annotations are saved as JSON and can be exported to VOC or COCO formats for segmentation and detection tasks.
Yes, the GUI includes video annotation support, allowing frame‑by‑frame labeling.
Predefine labels in a text file or configure them via the GUI; options include auto‑saving and validation.
Use the `--nodata` flag to store only relative image paths, keeping JSON files lightweight.
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