YOLO Algorithm, Object Detection with YOLOv3

Video Contents:
00:00 Introduction
00:51 Bounding Box Prediction
01:13 Box Parameters
03:20 Dimension Clusters
05:39 Box Assignment
06:21 Decision Tree for Box Assignment
07:20 Loss Function
07:27 Localization Loss
10:27 Confidence Loss
11:24 Classification Loss
14:04 Darknet-53
15:10 Predictions Across Scales
16:54 YOLOv3 Architecture Diagram

* YOLO Algorithm
* What is YOLO?
* Object Detection with YOLO Algorithm
* Details of Bounding Box Predictions using Anchor Boxes in YOLOv3
* Clustering Ground Truth Box Dimensions to obtain Anchor Box Dimensions
* Assignment of an Anchor Box to detect a Ground Truth Object
* Ignored predictions
* Details of Localization Loss, Confidence (Objectness) Loss and Classification Loss
* Details of Darknet-53
* How to obtain a Feature Extractor using Darknet-53
* Predictions Across 3 Scales
* Detailed diagram of YOLOv3

All images and animations in this video belong to me

Reference

YOLOv3: An Incremental Improvement
Joseph Redmon, Ali Farhadi

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