YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5.
Here is an overview of the
model:
YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5. YOLOv8, launched on January 10, 2023, features:
We benchmarked YOLOv8 on Roboflow 100, an object detection benchmark that analyzes the performance of a model in task-specific domains. Roboflow 100 is a method of effectively assessing the extent to which a model can generalize across different problems.
We found that YOLOv8 scored a 80.2% mAP score on Roboflow 100, compared to 73.5% mean score on YOLOv5. This shows that YOLOv8 is significantly better at domain-specific tasks than Ultralytics’ YOLOv5 predecessor. We compared YOLOv5s and YOLOv8 in this analysis.
(The table above is sourced from the official YOLOv8 repository).
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.
Roboflow offers a range of SDKs with which you can deploy your model to production.
YOLOv8
uses the
uses the
YOLOv8 PyTorch TXT
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.
You can automatically label a dataset using
YOLOv8
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
YOLOv8
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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