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
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