YOLOv4 Explained | CIOU Loss, CSPDarknet53, SPP, PANet | Everything about it

This video aims to explain YOLOv4, real-time object detection model including all features and techniques used in it. In this video, we thoroughly get into YOLOv4 architecture, its unique features such as the Dropblock, cross mini bn, SPP (Spatial Pyramid Pooling) module, CSP(cross stage partial connections) and how they all improves object detection performance. We start the video covering all features that improve backbone performance like cutmix, mosaic, label smoothing and cross stage partial connections. Each of these features are covered in great detail to give you an idea of how yolov4 works. Then dive deep into dropblock, ciou loss(complete iou loss), self adversarial training, grid sensitivity, diou nms and so on. We then end with a complete review of yolov4 architecture and performance of yolov4 to understand how it fares as a real time object detector specifically and also compare it to yolov3 ⏱️ Timestamps: 00:00 Intro 01:23 Typical Object Detection Model Architecture 03:03 YOLOv4 - Bag of freebies and Bag of specials 05:15 Cutmix Data Augmentation 07:10 Mosaic Data Augmentation 09:32 DropBlock Regularization in YOLOv4 20:19 Class Label Smoothing in YOLO-v4 23:40 Mish in Backbone 24:53 Cross Stage Partial Connections 29:26 MiWRC 31:27 Cross Mini Batch Normalization in YOLOv4 39:33 CIOU Loss (Complete IOU Loss) 47:47 Self Adversarial Training 49:11 Eliminating Grid Sensitivity in YOLO-v4 53:33 Genetic Algorithm 56:26 Spatial Pyramid Pooling 57:36 Spatial Attention Module for YOLOv4 59:50 Path Aggregation Network in YOLOv4 01:02:33 DIOU NMS 01:04:52 Performance of YOLOv4 01:05:43 YOLOv4 Architecture Explained 📖 Resources: YOLOv4 Paper - https://arxiv.org/pdf/2004.10934 YOLOv4 Repo - https://github.com/AlexeyAB/darknet Cutmix Paper - https://arxiv.org/pdf/1905.04899 Spatial Dropout Paper - https://arxiv.org/pdf/1411.4280 DropBlock Paper - https://arxiv.org/pdf/1810.12890 Mish Paper - https://arxiv.org/pdf/1908.08681 Cross stage Partial Connections Paper - https://arxiv.org/pdf/1911.11929 Efficient Det Paper - https://arxiv.org/pdf/1911.09070 Cross Iteration Batch Normalization Paper - https://arxiv.org/pdf/2002.05712 Generalized IOU Loss Paper - https://arxiv.org/pdf/1902.09630 DIOU and Complete IOU Loss Paper - https://arxiv.org/pdf/1911.08287 Grid Sensitivity Issue Link - https://github.com/AlexeyAB/darknet/i... Path Aggregation Paper - https://arxiv.org/pdf/1803.01534 🔔 Subscribe: https://tinyurl.com/exai-channel-link Email - [email protected]