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Leverage Multimodal Large Language Models (MLLMs) to automatically label your image and video datasets.
Generate high-quality bounding box annotations using state-of-the-art vision-language models like GPT-4V, Claude 3.5 Sonnet, and Qwen-VL. Save 80%+ annotation time while maintaining 85-95% accuracy.
Integrate with Label Studio for human review and collaborative annotation workflows.
🎉 Introducing advanced traffic sign detection powered by 188 high-resolution templates!
📁 signs/highres/png2560px/ → 188 traffic signs (2560×2560 PNG)
🔧 scripts/traffic_sign_detector.py → Multi-scale template matching
🔧 scripts/integrated_traffic_sign_pipeline.py → Complete detection & tracking pipeline
🔧 scripts/visualize_traffic_signs.py → Rich visualization and annotation
📚 TRAFFIC_SIGNS_INTEGRATION_GUIDE.md → Complete usage documentation
Quick Demo:
# Detect traffic signs in your video
python3 scripts/integrated_traffic_sign_pipeline.py \
--video your_traffic_video.mp4 \
--output traffic_signs_results.json \
--threshold 0.7
# Visualize results
python3 scripts/visualize_traffic_signs.py \
--video your_traffic_video.mp4 \
--detections traffic_signs_results.json \
--output annotated_video.mp4
# Run complete demo
python3 scripts/demo_traffic_sign_integration.pyFeatures:
- 🔍 188 Traffic Sign Templates: Complete Hong Kong traffic sign library
- 🎯 Multi-Scale Detection: 0.2x-2.0x scale range with NMS optimization
- 🚗 Real-Time Processing: 2-5 FPS on 1080p video
- 🏷️ Label Studio Compatible: Direct integration with annotation workflows
- 📊 Rich Visualization: Color-coded categories, confidence scores, track IDs
- ⚙️ Configurable: Adjustable thresholds, sampling rates, tracking parameters
📖 Complete Guide → TRAFFIC_SIGNS_INTEGRATION_GUIDE.md
video-autolabeling-pipeline/
├── README.md # Main documentation
├── LICENSE # Open source license
├── requirements.txt # Python dependencies
├── docs/ # 📚 Documentation
│ ├── 快速开始.md # Quick start guide (Chinese)
│ ├── QWEN_GUIDE.md # Qwen-VL detailed guide
│ ├── AUTO_LABELING_GUIDE.md # VLM auto-labeling guide
│ └── YOLO_GUIDE.md # YOLO local labeling guide
├── config/ # ⚙️ Configuration files
│ └── traffic_sign_config.json # 🚦 Traffic sign detection config
├── signs/ # 🚦 Traffic sign dataset
│ └── highres/png2560px/ # 188 traffic signs (2560×2560 PNG)
├── scripts/ # 🔧 Core scripts
│ ├── image_auto_labeling.py # Image auto-labeling
│ ├── video_auto_labeling.py # Video auto-labeling
│ ├── yolo_auto_labeling.py # YOLO labeling
│ ├── quick_yolo_label.sh # YOLO quick labeling script
│ ├── visualize_result.py # Visualize labeling results
│ ├── test_qwen_api.py # Test Qwen API
│ ├── start_label_studio.sh # Start Label Studio
│ ├── traffic_sign_detector.py # 🚦 Traffic sign detection
│ ├── integrated_traffic_sign_pipeline.py # 🚦 Complete traffic sign pipeline
│ ├── visualize_traffic_signs.py # 🚦 Traffic sign visualization
│ └── demo_traffic_sign_integration.py # 🚦 Demo script
├── templates/ # 📋 Labeling templates
├── data/ # 📹 Data files (examples)
└── labels/ # 🏷️ Labeling results (output)
First time user? → Check QUICKSTART.md for complete tutorial (10 mins setup)
Test with an image (fastest):
# 1. Set API Key (choose one)
export DASHSCOPE_API_KEY="your-qwen-key" # Qwen (recommended for China)
export ANTHROPIC_API_KEY="your-claude-key" # Claude (recommended for international)
# 2. Label an image
python3 scripts/image_auto_labeling.py your-image.jpg --provider qwen --visualize
# View the labeled result with bounding boxes instantly!💡 For detailed steps, see QUICKSTART.md or 快速开始.md (Chinese)
| Feature | Description |
|---|---|
| 🚦 Traffic Sign Detection | 188 traffic sign templates with multi-scale detection and tracking |
| 🤖 MLLM Auto-Labeling | Leverage GPT-4V, Claude 3.5 Sonnet, Qwen-VL to auto-generate labels |
| 📹 Video Frame Labeling | Smart sampling strategies for efficient video annotation |
| 🖼️ Image Object Detection | Single-shot bounding box generation for images |
| ⚡ Save 80%+ Time | AI generates initial labels, humans only review and refine |
| 🎯 High Accuracy | Traffic Signs: 85-95%, Claude: 90-95%, Qwen: 85-90%, YOLO: 80-85% |
| 🌐 China-Friendly | Hong Kong traffic signs + Qwen-VL support, no VPN required |
| 🔧 Production Ready | Label Studio integration, batch processing, visualization tools |
🚦 Traffic Sign Detection (NEW):
- TRAFFIC_SIGNS_INTEGRATION_GUIDE.md 🔥 NEW - Complete traffic sign detection guide
- TRAFFIC_SIGNS_INTEGRATION_SUMMARY.md - Project summary and technical details
For Beginners:
- QUICKSTART.md 🔰 Start Here - Complete tutorial, 10-min setup
- 快速开始.md ⭐ Quick Start - Three ways to get started (Chinese)
Advanced Guides:
- QWEN_GUIDE.md - Qwen-VL detailed guide (recommended for users in China)
- AUTO_LABELING_GUIDE.md - VLM auto-labeling with GPT-4V, Claude, etc.
- YOLO_GUIDE.md - YOLO local labeling (free, offline)
- 🚦 Traffic Sign Recognition: Automated detection and classification of 188+ Hong Kong traffic signs
- 🚗 Autonomous Driving: Vehicle, pedestrian, traffic sign detection
- 🏭 Industrial QA: Defect detection, product classification
- 🏥 Medical Imaging: Lesion annotation, organ segmentation
- 📦 E-commerce: Product recognition, shelf monitoring
- 🎥 Video Analytics: Action recognition, object tracking
We welcome Issues and Pull Requests! If you have questions or suggestions, please contact us on GitHub.
This project is licensed under the LICENSE file.