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…
Watch on YouTube ↗
(saves to browser)
Chapters (21)
Intro
1:23
Typical Object Detection Model Architecture
3:03
YOLOv4 - Bag of freebies and Bag of specials
5:15
Cutmix Data Augmentation
7:10
Mosaic Data Augmentation
9:32
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
1:02:33
DIOU NMS
1:04:52
Performance of YOLOv4
1:05:43
YOLOv4 Architecture Explained
DeepCamp AI