What is YOLO?

YOLO or You Only Look Once, is a popular real-time object detection algorithm. YOLO combines what was once a multi-step process, using a single neural network to perform both classification and prediction of bounding boxes for detected objects. As such, it is heavily optimized for detection performance and can run much faster than running two separate neural networks to detect and classify objects separately. It does this by repurposing traditional image classifiers to be used for the regression task of identifying bounding boxes for objects. This article will only look at YOLOv1, the first of the many iterations this architecture has gone through. Although the subsequent iterations feature numerous improvements, the basic idea behind the architecture stays the same. YOLOv1 referred to as just YOLO, can perform faster than real-time object detection at 45 frames per second, making it a great choice for applications that require real-time detection. It looks at the entire image at once, and only once — hence the name You Only Look Once — which allows it to capture the context of detected objects. This halves the number of false-positive detections it makes over R-CNNs which look at different parts of the image separately. Additionally, YOLO can generalize the representations of various objects, making it more applicable to a variety of new environments. Now that we have a general overview of YOLO, let’s take a look at how it really works.

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