![]() The third one is Single-Stage Deep Learning based algorithmsĪnd today, we are going to discuss the YOLO object detection family, which falls under the Single-Stage Deep Learning algorithms.Two-Stage Deep Learning based algorithms.There are three classes of algorithms in object detection: But to understand the magic behind these current best architectures, it is necessary to know how it all started and how far we have reached in the YOLO family. Since its inception, the object detection field has grown significantly, and the state-of-the-art architectures generalize pretty well on various test datasets. And today, we will give an introduction to the YOLO family by covering all the YOLO variants (e.g., YOLOv1, YOLOv2,…, YOLOX, YOLOR). YOLO (You Only Look Once) is a single-stage object detector introduced to achieve both goals (i.e., speed and accuracy). Hence, choosing an Object Detector that fits the bill for both speed and accuracy becomes essential. Many use cases, especially autonomous driving, require high accuracy and real-time inference speed. It is one such field that is not just limited to academia but has a potential real-world business use case in domains like video surveillance, healthcare, in-vehicle sensing, and autonomous driving. Most computer vision problems involve detecting visual object categories like pedestrians, cars, buses, faces, etc. Object detection is one of the most crucial subjects in computer vision. Improvements in Scaled-YOLOv4 over YOLOv4.What Are Single-Stage Object Detectors?.
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