The architecture of YOLOv4 includes several key components: the backbone, the neck, and the head. The backbone, which can be models like VGG, ResNet, or CSPDarknet53, is pre-trained to predict classes and bounding boxes. The neck, utilizing PANet, connects feature maps from different stages for comprehensive data extraction. Finally, the head, which uses configurations from YOLOv3, makes the final object detections. YOLOv4 also employs "bag of freebies" techniques like mosaic data augmentation and DropBlock regularization, further optimizing its speed and accuracy.