Grounding DINO is a zero-shot object detection model made by combining a Transformer-based DINO detector and grounded pre-training.
Here is an overview of the
model:
Grounding DINO is a zero-shot object detection model made by combining a Transformer-based DINO detector and grounded pre-training.
According to the Grounding DINO paper abstract, the model achieves "a 52.5 AP on the COCO detection zero-shot transfer benchmark".
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
GroundingDINO
uses the
annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
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