Overview

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.

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Performance

Use This Model

You can run fine-tuned YOLOv5 classification models with Inference.

First, install Inference:‍


pip install inference

Retrieve your Roboflow API key and save it in an environment variable called ROBOFLOW_API_KEY:


export ROBOFLOW_API_KEY="your-api-key"

To use your model, run the following code:


import inference
model = inference.load_roboflow_model("model-name/version")
results = model.infer(image="YOUR_IMAGE.jpg")

Above, replace:

YOUR_IMAGE.jpg with the path to your image.
model_id/version with the YOLOv5 model ID and version you want to use. Learn how to retrieve your model and version ID

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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