for Images & Videos
Leverage Multimodal Large Language Models to automatically generate high-quality bounding box annotations. Save 80%+ annotation time while maintaining 85-95% accuracy.
Production-ready tools for AI-powered image and video annotation
Leverage GPT-4V, Claude 3.5 Sonnet, and Qwen-VL to automatically generate bounding box labels with natural language understanding.
Smart sampling strategies for efficient video annotation. Process hours of footage in minutes with intelligent frame selection.
Single-shot bounding box generation for images. Get instant results with high-quality annotations.
AI generates initial labels in seconds. Humans only review and refine, saving 80%+ of annotation time.
Claude: 90-95%, Qwen: 85-90%, YOLO: 80-85%. Choose the right balance of accuracy and cost for your project.
Label Studio integration, batch processing, visualization tools. Everything you need for a complete annotation pipeline.
From zero to auto-labeled images in less than 5 minutes
export DASHSCOPE_API_KEY="your-qwen-key" # Qwen (China-friendly)export ANTHROPIC_API_KEY="your-claude-key" # Claude (Best quality)
✨ View the labeled result with bounding boxes instantly!
Choose the right model for your accuracy, speed, and budget requirements
Highest accuracy. Best for critical applications requiring precise annotations.
Best QualityGreat balance. Fast, affordable, and works perfectly in China without VPN.
RecommendedExcellent accuracy. Widely available and reliable for production use.
ReliableLocal processing. Completely free, works offline, fast inference speed.
Free & OfflineTrusted by teams across industries for various annotation tasks
Vehicle, pedestrian, and traffic sign detection
Defect detection and product classification
Lesion annotation and organ segmentation
Product recognition and shelf monitoring
Action recognition and object tracking
Crop monitoring and pest detection
Get started with MLLM auto-labeling in minutes