![]() ![]() Sign up or Log in to your Roboflow account to access state of the art dataset libaries and revolutionize your computer vision pipeline. Roboflow has free tools for each stage of the computer vision pipeline that will streamline your workflows and supercharge your productivity. ![]() It helps in understanding how the model detects and classifies multiple objects in a single pass. It was many times faster than the popular two-stage detectors like Faster-RCNN but at the cost of lower accuracy.Ī comprehensive object-annotated image dataset is essential for grasping the YOLOv1 object detection model. YOLOv1, an anchor-less architecture, was a breakthrough in the Object Detection regime that solved object detection as a simple regression problem. From our previous post, “Introduction to YOLO family,” we know that object detection is divided into three classes of algorithms: traditional computer vision, two-stage detectors, and single-stage detectors.Īnd today, we are going to discuss one of the first single-stage detectors called Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1). Object detection has become increasingly popular and has grown widely, especially in the Deep Learning era. Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1)
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