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Documentation
- English
- 中文文档
Key Features
- 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
Supported Data Formats
Category | Format | Status | Description |
---|---|---|---|
Detection | COCO8 | ✅ Supported | A small dataset with 8 COCO images (4 train, 4 val) for object detection |
COCO128 | ⏳ Planned | First 128 images of COCO train2017 dataset for object detection testing | |
Segmentation | COCO8-seg | ✅ Supported | 8 COCO images with instance segmentation annotations |
COCO128-seg | ⏳ Planned | 128 COCO images with segmentation masks for testing | |
Pose | COCO8-pose | 🔜 Coming Soon | 8 COCO images with keypoints annotations for pose estimation |
Tiger-pose | ⏳ Planned | 263 tiger images with 12 keypoints per tiger | |
Classification | MNIST160 | 🔜 Coming Soon | First 8 images of each MNIST category (160 images total) |
ImageNet-10 | ⏳ Planned | Smaller subset of ImageNet with 10 categories | |
OBB | DOTA8 | ⏳ Planned | Small subset of 8 aerial images with oriented bounding boxes |
Multi-Object Tracking | VisDrone | ⏳ Planned | Drone imagery for tracking multiple objects across frames |
🌟 Explore More Dataset Formats 🌟
Ultralytics supports a comprehensive range of datasets
Detection · Segmentation · Pose · Classification · Tracking
COCO · VOC · ImageNet · DOTA · and many more
Why This Extension?
- 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
Requirements
- Visual Studio Code 1.85.0 or higher
- Image files in your workspace
- YAML configuration files for YOLO annotations
Installation
- Open VS Code
- Press
Ctrl+P
to 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.
Usage
- 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
Interface Controls
- 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
🆕 Label Interaction (since 0.0.48)
- Box/Seg Highlight & Move:
- Hold
Ctrl
and hover the mouse to highlight a box or segmentation (seg) region. - Highlighted box/seg will show a flowing dashed border for clear visual feedback.
- Hold
Ctrl
and drag with the left mouse button to move the highlighted box/seg. - If
Ctrl
is not pressed, you cannot select, move, or highlight any label. This prevents accidental operations.
- Hold
Keyboard Shortcuts
Ctrl+Y
: Open YOLO Labeling Panel
In Labeling Panel:
Ctrl + Mouse Hover
: Highlight box/segCtrl + Mouse Drag
: Move highlighted box/segD
: 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)
Search Functionality:
Arrow Down
: Move down through search resultsArrow Up
: Move up through search resultsEnter
: Select the highlighted search resultEscape
: Close search results panel
Extension Settings
This extension contributes the following commands:
yolo-labeling-vs.openLabelingPanel
: Open YOLO Labeling Panel
Known Issues
Please report issues on our GitHub repository.
Release Notes
0.0.9
- 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
0.0.8
- Fixed theme showcase display in GitHub readme (changed from grid to table layout)
- Improved documentation formatting for better platform compatibility
0.0.7
- Significantly reduced extension package size (from 51MB to 1.5MB)
- Updated documentation to use GitHub hosted images
- Improved extension loading performance
0.0.6
- 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
0.0.5
- 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
0.0.4
- Simplified keyboard shortcuts for better usability
- Changed main shortcut from
Ctrl+Shift+Y
toCtrl+Y
for easier access - Removed
Ctrl+Right
andCtrl+Left
shortcuts - Improved UI by hiding scrollbar in label list
- Reduced package size by excluding test data files
0.0.3
Initial release of YOLO Labeling:
- Basic image labeling functionality
- YOLO format support
- Keyboard shortcuts
- Configuration file support
Contributing
We welcome contributions! Please feel free to submit a Pull Request.
Sponsorship
If you find this extension helpful, consider supporting its development:
WeChat Pay | Alipay |
---|---|
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License
This project is licensed under the MIT License - see the LICENSE file for details.
Support
If you encounter any problems, please file an issue at our issue tracker.