A powerful, user-friendly video labeling software for machine learning practitioners and researchers. Create high-quality training datasets for YOLO, Faster R-CNN, and other object detection models.
Windows users: Download and run directly - No installation or Python required!
Download AnnotateX.exe (Windows Executable)
- Load and navigate video files (MP4, AVI, MOV)
- Draw bounding boxes with click and drag
- 80+ pre-defined COCO classes + custom classes
- Export annotations in multiple formats:
- YOLO
- Pascal VOC
- COCO JSON
- Frame-by-frame navigation
- Copy annotations from previous frame
- Zoom and pan support
- Keyboard shortcuts for efficient workflow
pip install opencv-python pillow numpy
python video_annotating.py- Python 3.8+
- OpenCV
- Pillow
- NumPy
- Tkinter (included with Python)
| Key | Action |
|---|---|
| ← → | Navigate frames |
| Space | Next frame |
| Delete | Delete selected box |
| Ctrl+Z | Undo |
| Ctrl+C | Copy from previous frame |
| Scroll | Zoom in/out |
| Escape | Deselect |
| Home/End | First/Last frame |
- YOLO: Normalized coordinates
(class_id, x_center, y_center, width, height) - Pascal VOC: XML format for object detection
- COCO: JSON format with full annotations
A sample video is included in the Sample_Video/ folder to help you get started and test the software.
- Click Open to load a video file (or use the sample video in
Sample_Video/) - Select a class from the left panel
- Click and drag on the video to draw bounding boxes
- Use arrow keys or timeline to navigate frames
- Click Save to export annotations
MIT License
If you use this tool in your research, please cite this:
@Article{smartcities6050134,
AUTHOR = {Shokri, Danesh and Larouche, Christian and Homayouni, Saeid},
TITLE = {A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems},
JOURNAL = {Smart Cities},
VOLUME = {6},
YEAR = {2023},
NUMBER = {5},
PAGES = {2982--3004},
DOI = {10.3390/smartcities6050134}
}Shokri, Danesh, Christian Larouche, and Saeid Homayouni. 2023. "A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems" Smart Cities 6, no. 5: 2982-3004. https://doi.org/10.3390/smartcities6050134