Description
YOLOv1 is a single-stage object detection model. Object detection is framed as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.
The network uses features from the entire image to predict each bounding box. It also predicts all bounding boxes across all classes for an image simultaneously. This means the network reasons globally about the full image and all the objects in the image.
Papers Using This Method
ODVerse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v112025-02-20YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain2024-06-14YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series2024-06-12Machine learning approach of automatic identification and counting of blood cells2019-09-05Light-Weight RetinaNet for Object Detection2019-05-24You Only Look Once: Unified, Real-Time Object Detection2015-06-08