ByteTrack is a multi-object tracking computer vision model. Using ByteTrack, you can allocate IDs for unique objects in a video for use in tracking objects. For example, you can track players on a football field and monitor them throughout a scene.

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

Explore this model on Roboflow

No items found.

Deploy ByteTrack to production

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

No items found.

ByteTrack Annotation Format

ByteTrack

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.
‍
‍

Convert data between formats

Label data automatically with ByteTrack

You can automatically label a dataset using

ByteTrack

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

ByteTrack

to train a computer vision model.

No items found.