YOLO Object Detection | YoloV1 Explanation and Implementation Tutorial

This video is on YOLO object detection, specifically yolov1 object detection algorithm. In this tutorial we try to understand how the YOLO algorithm works, from its real-time object detection capabilities to its approach of bounding box predictions. We will also go through YOLOv1 implementation from scratch in PyTorch. By the end of this video you would be able to get a complete explanation of YOLOv1 paper, how Yolo algorithm works, different parts of multi part loss used for training yolo and and how to train and implement YOLOv1 for object detection. ⏱️ Timestamps: 00:00 Intro 00:37 One Stage vs Two Stage Object Detection 02:05 Yolo Object Detection Algorithm 04:09 Yolo Bounding Box Prediction 06:22 Grid Cell Predictions 09:43 Yolo Architecture Explained 12:25 Yolo Algorithm Loss Function 22:08 Defining Targets for Yolo Predictions 25:06 Yolo Training Summary 27:37 Yolov1 Implementation from Scratch 35:27 Yolov1 Model Implementation 37:53 Yolo Object Detection Loss Implementation 47:02 Yolov1 Training and Inference Code 49:53 Yolo Object Detection Results 📖 Resources: Yolo Paper - https://tinyurl.com/exai-yolov1-paper Github Implementation Link - https://tinyurl.com/exai-yolov1-implementation 🔔 Subscribe : https://tinyurl.com/exai-channel-link Background Track - Fruits of Life by Jimena Contreras Email - [email protected]