It can automatically identify and locate various objects in the image
YOLO-World outperforms many of the most advanced methods in terms of speed and accuracy.
Zero sample detection capabilities allow real-time target detection without training, even if certain items have not been seen before.
Main features:
1. Large-scale learning: YOLO-World has gained rich visual knowledge and language knowledge by learning a large number of pictures and corresponding descriptions (such as item names), which allows it to identify a wide range of items.
The project was pre-trained on large-scale visual-language datasets including Objects365, GQA, Flickr 30K and CC3M, giving YOLO-World strong zero-sample open vocabulary capabilities and positioning capabilities in images.
2. Fast and accurate: YOLO-World achieved 35.4 AP in zero-sample evaluation on the LVIS dataset and a processing speed of 52.0 FPS on the V100, which exceeds many of the most advanced methods in speed and accuracy. Maintain high accuracy even in pictures containing complex scenes. YOLO-World claims to be 20 times faster than GroundingDINO.
3. Zero-sample testing: The most impressive thing is that even if YOLO-World has not seen some items before, it can successfully identify and locate these new items through clues and contextual information in the pictures based on its previous learning and understanding capabilities.
4. Understand objects: YOLO-World not only relies on visual information, but also combines verbal information. It understands human language descriptions, which allows it to identify even objects that it has not directly seen before.
Projects and demonstrations:http://yoloworld.cc
Thesis:https://arxiv.org/abs/2401.17270
GitHub：https://github.com/AILab-CVC/YOLO-World
Online experience:https://huggingface.co/spaces/stevengrove/YOLO-World
Video: youtu.be/I2aW-jPqilM