DEtection TRansformer (DETR) vs. YOLO for object detection.
Ever wondered how computers can analyze images, identifying and localizing objects within them? That’s exactly what object detection accomplishes in the world of computer vision. DEtection TRansformer (DETR) and You Only Look Once (YOLO) are the two prominent approaches for object detection. YOLO has earned its reputation as the go-to model for real-time object detection and tracking problems. Meanwhile, DETR, a rising contender powered by transformer technology, has the potential to revolutionize computer vision, similar to its impact on natural language processing. In this blog post, I will explore these two methods to understand how they work their magic!
Since 2012, computer vision has undergone a revolutionary transformation driven by the arrival of Convolutional Neural Networks (CNNs) and deep learning architectures. Notable among these architectures are AlexNet (2012), GoogleNet (2014), VGGNet (2014), and ResNet (2015), which incorporated numerous convolutional layers to enhance image classification accuracy. While image classification task involves assigning labels to entire images, like categorizing a picture as a dog or a car, object detection not only identifies what’s in an image but also pinpoints where each object is located within that image.
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