YOLOv5 Image Classification — Complete Tutorial

Introduction — Why Use YOLOv5 for Image Classification?

When most developers hear “YOLO,” they think of real-time object detection — boxes around cars, people, or animals.
But in recent versions, YOLOv5 introduced something equally powerful: YOLOv5-cls, a classification-only mode designed to label entire images instead of detecting objects.

In this mode, YOLOv5 combines the same speed and accuracy that made it famous for detection, with the simplicity of classical image classification.
It runs on the PyTorch framework, supports GPU acceleration through CUDA, and can outperform many older CNNs like VGG or ResNet when properly trained.

In this tutorial, you’ll build a complete YOLOv5 image classification pipeline using the Animals10 dataset from Kaggle — a collection of over 26,000 animal photos categorized into 10 species such as cats, dogs, horses, squirrels, and butterflies.
We’ll start from environment setup, continue through dataset preparation and model training, and finish by predicting new images interactively with OpenCV.

By following this post, you’ll learn how to:

• Evaluate and visualize predictions directly in Python.