Models

What is YOLOv5 Classification?

YOLOv5 Classification is a version of the YOLOv5 model used in single-label and multi-label image classification.

About the model

Here is an overview of the

YOLOv5 Classification

model:

Date of Release Sep 01, 2022
Model Type Classification
Architecture
Framework Used
Annotation Format
Stars on GitHub 46000+

YOLOv5 for Classification

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 with YOLOv5

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 benefits

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.

YOLOv5 performance
YOLOv5 Size

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

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

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

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

Model Performance

Explore this model on Roboflow

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Deploy YOLOv5 Classification to production

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

YOLOv5 Classification Annotation Format

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.

Convert data between formats

Label data automatically with YOLOv5 Classification

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.

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