A smart annotation tool that uses YOLOE (YOLO Everything) to accelerate the labeling process. Label one image, and let AI help you label the rest!
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Smart Labeling with YOLOE:
- Draw bounding box on the first image
- AI automatically detects similar objects in subsequent images
- Learns from your initial annotation to speed up the labeling process
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Interactive Annotation:
- Load and browse through images from a folder
- Draw bounding boxes with click-and-drag
- Visual prompt-based object detection
- Review and adjust AI-generated annotations
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Efficient Workflow:
- Start by annotating just one example
- AI generates labels for remaining images
- Review and correct AI predictions if needed
- Save annotations in YOLO format
- Python 3.8+
- PyQt5
- YOLOE dependencies
- OpenCV
- numpy
- Clone this repository
- Install the required packages:
pip install -r requirements.txt- Run the application:
python main.py-
First Image Annotation:
- Load your image folder
- Draw a bounding box around your object of interest
- Enter the class name for the object
- This becomes the visual prompt for YOLOE
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Automatic Labeling:
- YOLOE uses your first annotation as a reference
- Automatically detects and labels similar objects
- Results are shown for review
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Review and Save:
- Review AI-generated labels
- Adjust or correct if needed
- Save final annotations in YOLO format
The tool saves annotations in YOLO format:
<object-class> <x-center> <y-center> <width> <height>
- All values are normalized between 0 and 1
- Object class is an integer starting from 0
- (x-center, y-center) represents the center of the bounding box
- (width, height) represents the width and height of the bounding box
- Next Image: Right Arrow
- Previous Image: Left Arrow
- Save: Ctrl+S
- Reset Box: R
- Quit: Q
- Auto-training integration
- Automatically fine-tune YOLOE on your dataset
- Improve detection accuracy with more examples
- Export trained model for inference
- Batch processing capabilities
- Multi-class support in single session
- Export to various formats
- Active learning integration
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Visual Prompting:
- Your first annotation serves as a visual prompt
- YOLOE learns what to look for from this example
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AI Detection:
- YOLOE processes subsequent images
- Uses visual similarity to find matching objects
- Generates bounding box predictions
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Future: Auto Training:
- Collect verified annotations
- Fine-tune YOLOE on your specific dataset
- Improve detection accuracy over time
Contributions are welcome! Please feel free to submit pull requests.