From the course: Deep Learning and Computer Vision: Object Detection with PyTorch

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Object detection models and their types

Object detection models and their types - PyTorch Tutorial

From the course: Deep Learning and Computer Vision: Object Detection with PyTorch

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Object detection models and their types

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- [Presenter] Welcome back. In this video, we'll explore different object detection models and understand what makes each of them unique. Object detection is not a one size fits all solution. Instead, various models are designed to prioritize either speed, accuracy, or a combination of both. There are several object detection models, each with its own strengths. These models have been developed to address different needs based on various real-world applications. For example, a self-driving car needs a model that's fast, while a security system might prioritize accuracy. Let's start with the YOLO, which stands for You Only Look Once. As the name suggests, YOLO processes an entire image in a single pass, predicting all bounding boxes and classes at once. This makes YOLO extremely fast compared to other models. Due to its speed, YOLO is either for realtime applications like self-driving cars where quick decisions are essential, or surveillance systems that needs to track multiple objects…

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