The critical differences between YOLOv2, YOLOv3, and YOLOv4 lie in their architectures, performance, and features. YOLOv2 introduced the Darknet-19 architecture, improved anchor boxes, and multi-scale training, enhancing detection accuracy. YOLOv3 further refined detection performance with Darknet-53 architecture, feature pyramid network, and additional improvements in box prediction. YOLOv4, built upon YOLOv3, incorporated advancements like CSPNet, PANet, and improved training techniques, achieving superior detection accuracy and speed. While YOLOv4 introduced more robustness, YOLOv3 remains famous for its balance between accuracy and speed. Implementations vary with OpenCV, PyTorch, or other frameworks, each offering different arguments for efficiency and ease of use.