Dear Students and YouTube Family!
In this video, you will get to know the YOLO v3 (You Only Look Once) algorithm which is used for object detection. Object detection is a critical capability which is more prominent in different walks of life from last some decades. YOLO is a clever algorithm with CNN network architecture for performing object detection in real-time.
In this video we will understand the theory behind how exactly YOLO algorithm works. In next video we will dive deep towards a python implementation of algorithm to perform object detection on pre-trained as well as custom trained model.

In this theoretical part of the tutorial following topics will be covered:
00:00 Preview and Content list
03:15 Introduction to Object Detection
06:29 What is YOLO?
08:23 Key Concepts for understanding YOLO
09:09 Residual blocks – Key Concept I
12:56 Localization – Key Concept II
14:38 Bounding boxes – Key Concept III
19:34 Target Vector y – Key Concept IV
25:09 Anchor boxes / Priors – Key Concept V
31:16 IOU – Key Concept VI
35:11 Non Max Suppression Algorithm
40:58 Overview of Network Architecture of YOLO
52:11 Loss function of YOLO v3
01:03:06 Applications of YOLO Algorithm
01:07:55 Benefits of YOLO Algorithm
01:10:19 Limitations of YOLO Algorithm
01:11:06 End Notes of Topic

Please visit the following link for its python implementation:

Researchers: Ms. Nayab Tahir, Ms. Noshiza Mukhtar, Ms. Khadija Akhter, Ms. Simran Parveen

#tazzainamalik #yoloalgorithm #yoloobjectdetectionalgorithm #objectdetectiondeeplearning
#objectdetection

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