YOLOv2 (YOLO9000) and YOLOv3 Explained

In this yolo object detection series tutorial, we dive into the details of YOLOv2 (YOLO9000) and YOLOv3 model for object detection . The video explores how yolov2 and yolov3 models work, their architectures, losses for training them, and their advancements over earlier versions like YOLOv1. We will get into features that make YOLOv2 better, faster, and stronger, as described in the YOLO9000 paper, and how it improved object detection performance with techniques like anchor boxes, selection of priors using clustering and so on. We will also explore YOLOv3, its architecture, and its enhancements, including its ability to make predictions at multiple scales using feature pyramids. Topics covered in this video: - How YOLOv2 (YOLO9000) works and its key advancements. - YOLOv2 training for object detection. - YOLOv2 architecture and its improvements over YOLOv1. - YOLOv2 loss - YOLOv3 architecture and its improvements over YOLOv2. - Comparison: YOLOv1 vs YOLOv2 vs YOLOv3 The attempt is to ensure that by the end of this video, we have a clear understanding of the evolution of YOLO, from YOLOv2's real-time capabilities with high accuracy to YOLOv3's multi-scale detection ⏱️ Timestamps: 00:00 Intro 00:46 Recap of YOLOv1 05:57 YOLOv2 Better 12:31 Clustering for prior boxes in YOLOv2 17:50 Box prediction in YOLOv2 21:26 Passthrough Layer in YOLOv2 25:20 Multi Scale Training of YOLOv2 26:47 YOLOv2 architecture 29:52 YOLO9000 | Making YOLOv2 stronger 39:18 YOLOv2 Loss for training 43:43 YOLOv3 architecture 51:25 YOLOv3 performance for object detection 📖 Resources: Yolov2 Paper - https://tinyurl.com/exai-yolov2-paper Yolov3 Paper - https://tinyurl.com/exai-yolov3-paper 🔔 Subscribe : https://tinyurl.com/exai-channel-link Email - explainingai.official@gmail.com