YOLOv5 Classification is a version of the YOLOv5 model used in single-label and multi-label image classification.
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 classification (August 2022) and instance segmentation (September 2022).
Classification assigns a given image to an array of possible classes and can be binary or multi-class. Using classification to identify one particular class could mean you could train a model to identify a specific fruit and then pass images of plants through the model to identify what fruit is in the image. With multi-class classification, you may want to know each class represented in the image.
Classification do not localize in the image where the objects of interest are, how many there are, or their size.
Find a free classification dataset to try YOLOv5 for classification.
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. This means you can use YOLOv5 classification on the edge on devices like iPhones or cameras. 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 Classification
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 Classification
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 Classification
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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