Grounding DINO model (base variant) The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre Training for Open Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed set object detection model with a text encoder, enabling open set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero shot. Grounding DINO overview. Taken from the original paper . Intended uses & limitations You can use the raw model for zero shot object detection (the task of detecting things in an image out of the box without labeled data). How to use Here's how to use the model for zero shot object detection: BibTeX entry and citation info
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy