DEtection TRansformer (DETR) vs. YOLO for object detection
<p>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. <a href="https://arxiv.org/abs/2005.12872" rel="noopener ugc nofollow" target="_blank">DEtection TRansformer</a> (DETR) and <a href="https://arxiv.org/abs/1506.02640" rel="noopener ugc nofollow" target="_blank">You Only Look Once</a> (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!</p>
<p>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 <a href="https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks" rel="noopener ugc nofollow" target="_blank">AlexNet (2012)</a>, <a href="https://arxiv.org/abs/1409.4842" rel="noopener ugc nofollow" target="_blank">GoogleNet (2014)</a>, <a href="https://arxiv.org/abs/1409.1556" rel="noopener ugc nofollow" target="_blank">VGGNet (2014)</a>, and <a href="https://arxiv.org/abs/1512.03385" rel="noopener ugc nofollow" target="_blank">ResNet (2015)</a>, 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.</p>
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