YOLOv5 Instance Segmentation is a version of YOLOv5 that can be used for instance segmentation tasks.
Here is an overview of the
model:
YOLOv5 was released by Glenn Jocher on June 9, 2020 for object detection. Recently, YOLOv5 added support for instance segmentation (September 2022) and classification (August 2022).
Instance segmentation (also known as image segmentation) is the computer vision task of recognizing objects in images along with their associated shape. It's useful in cases where you need to measure the size of detected objects, cut them out of their background, or more accurately detect oblong rotated objects.
Find a free instance segmentation dataset to try YOLOv5 for instance segmentation.
If you have your own data, label your images for free using Roboflow Annotate.
YOLOv5 is regarded as smaller and generally easier to use in production thanks to being implemented in Pytorch. Read more about YOLOv5 performance.
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.
YOLOv5 Instance Segmentation
uses the
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.
You can automatically label a dataset using
YOLOv5 Instance Segmentation
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
YOLOv5 Instance Segmentation
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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