🚀 Visual Prompt to Object Detection & Segmentation without Training - YOLOvX 🚀 #ai #computervision

Object Detection & Segmentation without Training

YOLO are fast and accurate but limited to predefined categories. Open-set methods address this but often sacrifice efficiency. YOLOE changes this by integrating text, visual, and prompt-free detection into a single, highly efficient model—achieving real-time “see anything” capability, check YOLOvX Demos below.👇🏻
🔹 Text Prompts – RepRTA refines embeddings with zero inference overhead.
🔹 Visual Prompts – SAVPE enhances visual embeddings with minimal complexity.
🔹 Prompt-Free – LRPC detects all objects efficiently, without language model dependencies.

💡 Why YOLOE?
✅ 3× lower training cost & 1.4× faster inference vs. YOLO-Worldv2-S (+3.5 AP on LVIS)

🎯 Real-World Use Cases:
🔍 Industrial Conveyors – Automate real-time object detection & quality control for manufacturing lines. (See below conveyor videos for detecting oranges and caps in motion!)
🚗 Autonomous Vehicles – Identify unknown objects on the road.
🛑 Surveillance – Detect threats dynamically.
🛒 Retail – Adapt checkout systems for any product.
🏥 Healthcare – Assist in anomaly detection.

Awesome work by: ao wang, Lihao Liu, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding, Piotr Skalski

🔔 Stay tuned for more exciting developments and breakthroughs on the horizon! ✨

WISERLI YOLOvX OpenCV Roboflow Ultralytics

Dr. Chandrakant Bothe Rohan Gupta Vishnu Mate Mohit Raj Sinha Prateeksha Tripathy Sinem Çelik Sharda Jadhav Neetu Shaw Anu Bothe Saurabh Tople Glenn Jocher Muhammad Rizwan Munawar Nicolai Nielsen Harpreet Sahota 🥑 Ritesh Kanjee Piotr Skalski Dragos Stan Arnaud Bastide Timothy Goebel Bharath kumar Sean Carnahan

#ComputerVision #YOLOE #ObjectDetection #AI #YOLOvX #MachineLearning