CoDet is an open vocabulary zero-shot object detection model. The model was described in the "CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection" published by Chuofan Ma, Yi Jiang, Xin Wen, Zehuan Yuan, Xiaojuan Qi. The paper was submitted to NeurIPS2023.

Check out YOLOv8, defining a new state-of-the-art in computer vision

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.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Model Performance

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Deploy CoDet to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

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CoDet Annotation Format

CoDet

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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Convert data between formats

Label data automatically with CoDet

You can automatically label a dataset using

CoDet

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

CoDet

to train a computer vision model.

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