A specialized tool for visualizing bounding boxes and labels across major computer vision annotation formats.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install annotation-visualizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install annotation-visualizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Annotation Visualizer?
Annotation Visualizer provides a seamless way to verify the accuracy of your computer vision training data. By integrating with the Openclaw Skills repository, this tool bridges the gap between raw annotation files and visual validation. It renders bounding boxes and labels directly onto your images, allowing developers to catch errors in YOLO, COCO, VOC, and LabelMe formats before they impact model performance.
This utility is particularly useful for identifying coordinate shifts, mislabeled classes, or missing annotations in complex datasets. As part of your broader Openclaw Skills workflow, it automates the tedious task of manually inspecting JSON and XML files by transforming them into intuitive visual outputs.
Annotation Visualizer Use Cases
- Inspecting YOLO or COCO datasets for labeling errors before model training.
- Validating the output of automated labeling agents or manual annotators.
- Creating visual documentation of dataset contents for project reporting.
- Debugging transformation pipelines that convert between different annotation formats.
How Annotation Visualizer Works
- Specify the dataset format (YOLO, COCO, VOC, or LabelMe) via the command line.
- Point the script to your image directory and the corresponding annotation files.
- Apply custom styles such as box thickness, font size, and color schemes using optional flags.
- Run the visualizer to generate a parallel directory of annotated images for quality assurance.
Annotation Visualizer Setup
To get started with this component of Openclaw Skills, install the necessary dependencies via pip:
pip install pillow
Run the visualizer with a command similar to the following:
python scripts/visualize.py coco annotations.json images/ output/
Annotation Visualizer Data Schema & Taxonomy
The Annotation Visualizer handles specific file structures based on the format chosen:
| Format | Extension | Structure Detail |
|---|---|---|
| YOLO | .txt | Space-separated class and normalized coordinates. |
| COCO | .json | A central JSON file containing 'images', 'annotations', and 'categories'. |
| VOC | .xml | XML files following the Pascal VOC schema per image. |
| LabelMe | .json | Polygonal or rectangular data stored in image-specific JSONs. |
Annotation Visualizer Advanced Features
- Support for semi-transparent box fills to enhance visibility in crowded scenes.
- Automatic or manual class color assignment for consistent multi-class visualization.
- High-speed batch processing for massive datasets containing thousands of images.
- Customizable label overlays including confidence scores and class names.
SKILL.md
Loading
Related Openclaw Skills
Annotation Format Converter
A versatile tool to bridge the gap between different computer vision annotation standards like COCO, YOLO, and VOC.
WSB Digest
An automated tool to track trending WallStreetBets stocks and deliver insightful daily reports directly to Discord.
Vue Table Automation and Excel Export
A specialized automation skill for navigating vue-element-admin tables, updating record importance, and generating Excel reports.
Universal PDF Vision Parser
A high-performance document digitizer that uses multimodal vision to convert multilingual PDFs and complex layouts into perfectly structured Markdown.
Batch Rename
A powerful script for batch renaming image datasets and their associated annotation files using flexible numbering and naming patterns.
Bazi Pan - Eight Characters Fortune Telling
A sophisticated AI-driven charting tool for Bazi Eight Characters analysis using traditional Chinese metaphysics logic.
Featured*
