A VS Code extension for quickly browsing and editing YOLO dataset annotations. This extension allows you to efficiently view and modify YOLO-formatted labels through YAML configuration files, making it easy to manage your computer vision datasets directly within VS Code.
Our extension seamlessly integrates with all VS Code themes for a consistent experience:
- English
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- Quick Dataset Browsing: Instantly view YOLO-labeled images through YAML configuration files
- Efficient Label Management: Easily modify existing labels without leaving VS Code
- Intuitive Preview: Real-time visualization of bounding boxes and labels
- Streamlined Navigation: Quick movement between images using keyboard shortcuts
- YAML Integration: Direct support for YAML configuration files
- Batch Processing: Browse and edit multiple images in sequence
| Category | Format | Status | Description |
|---|---|---|---|
| Detection | COCO8 | β Supported | A small dataset with 8 COCO images (4 train, 4 val) for object detection |
| COCO128 | β³ Planned | First 128 images of COCO train2017 dataset for object detection testing | |
| Segmentation | COCO8-seg | β Supported | 8 COCO images with instance segmentation annotations |
| COCO128-seg | β³ Planned | 128 COCO images with segmentation masks for testing | |
| Pose | COCO8-pose | π Coming Soon | 8 COCO images with keypoints annotations for pose estimation |
| Tiger-pose | β³ Planned | 263 tiger images with 12 keypoints per tiger | |
| Classification | MNIST160 | π Coming Soon | First 8 images of each MNIST category (160 images total) |
| ImageNet-10 | β³ Planned | Smaller subset of ImageNet with 10 categories | |
| OBB | DOTA8 | β³ Planned | Small subset of 8 aerial images with oriented bounding boxes |
| Multi-Object Tracking | VisDrone | β³ Planned | Drone imagery for tracking multiple objects across frames |
Ultralytics supports a comprehensive range of datasets
Detection Β· Segmentation Β· Pose Β· Classification Β· Tracking
COCO Β· VOC Β· ImageNet Β· DOTA Β· and many more
- Simplified Workflow: No need to switch between different tools - view and edit YOLO datasets directly in VS Code
- Developer-Friendly: Perfect for ML engineers who want to quickly verify or adjust their YOLO datasets
- Lightweight: Fast and responsive, designed for handling large datasets
- Integrated Experience: Seamlessly fits into your development environment
- Visual Studio Code 1.85.0 or higher
- Image files in your workspace
- YAML configuration files for YOLO annotations
- Open VS Code
- Press
Ctrl+Pto open the Quick Open dialog - Type
ext install andaoai.yolo-labeling-vs - Press Enter to install
Or you can install it directly from the VS Code Marketplace.
- Open a folder containing your YAML configuration files and corresponding images
- Right-click on a YAML file in the explorer
- Select "Open YOLO Labeling Panel"
- Browse through your labeled images and make adjustments as needed
- Previous/Next Image: Navigate through images in the dataset
- Mode Selector: Switch between Box and Segmentation labeling modes
- Show Labels: Toggle visibility of labels on the image
- Save Labels: Save current annotations to disk
- Search Box: Search for specific images in the dataset
Ctrl+Y: Open YOLO Labeling Panel
D: Go to next imageA: Go to previous imageCtrl+S: Save labelsCtrl+Z: Undo the last labeling actionCtrl+Wheel: Zoom in/out at mouse positionAlt+Drag: Pan the image when zoomed inWheel: Scroll vertically when zoomed inShift+Wheel: Scroll horizontally when zoomed inRight-click: Cancel polygon drawing (in segmentation mode)
Arrow Down: Move down through search resultsArrow Up: Move up through search resultsEnter: Select the highlighted search resultEscape: Close search results panel
This extension contributes the following commands:
yolo-labeling-vs.openLabelingPanel: Open YOLO Labeling Panel
Please report issues on our GitHub repository.
- Added visual feedback for unsaved changes with pulsing save button animation
- Added tooltip showing "Changes need saving" when hovering over save button
- Improved UI button states with better visual feedback
- Enhanced error handling with more detailed suggestions
- Added image dimensions and label counts to UI for better information display
- Fixed theme showcase display in GitHub readme (changed from grid to table layout)
- Improved documentation formatting for better platform compatibility
- Significantly reduced extension package size (from 51MB to 1.5MB)
- Updated documentation to use GitHub hosted images
- Improved extension loading performance
- Added seamless VSCode theme integration with proper button styling
- Added theme showcase with support for multiple VSCode themes
- Improved UI responsiveness across all theme variants
- Fixed button styling issues to properly follow VSCode theme changes
- Enhanced visual consistency across light and dark themes
- Removed redo functionality button to avoid conflicts with Ctrl+Y shortcut
- Improved error handling when YAML image paths fail to load
- Added error tracking with recovery mechanisms
- Implemented proper resource cleanup
- Added reload button on error pages
- Enhanced error messages with troubleshooting guidance
- Added tooltips for all toolbar buttons showing keyboard shortcuts
- Added better error messaging and recovery options
- Simplified keyboard shortcuts for better usability
- Changed main shortcut from
Ctrl+Shift+YtoCtrl+Yfor easier access - Removed
Ctrl+RightandCtrl+Leftshortcuts - Improved UI by hiding scrollbar in label list
- Reduced package size by excluding test data files
Initial release of YOLO Labeling:
- Basic image labeling functionality
- YOLO format support
- Keyboard shortcuts
- Configuration file support
We welcome contributions! Please feel free to submit a Pull Request.
If you find this extension helpful, consider supporting its development:
| WeChat Pay | Alipay |
|---|---|
This project is licensed under the MIT License - see the LICENSE file for details.
If you encounter any problems, please file an issue at our issue tracker.