Scaled YOLOv4

Scaled YOLOv4 is an advanced extension of the YOLOv4 object detection model, developed by Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao, and implemented in the YOLOv5 PyTorch framework. It leverages Cross Stage Partial Networks to enhance scalability in depth, width, resolution, and structural dimensions while maintaining speed and accuracy. Licensed under GPL-3.0, Scaled YOLOv4 can be deployed on various hardware platforms, including CPU and GPU devices, with support from Roboflow's SDKs for production deployment. It facilitates automatic dataset labeling through Autodistill and offers a no-code, GPU-backed deployment option for interactive workflows. Roboflow provides resources for users to train custom object detection models with Scaled YOLOv4 and convert data between formats using its annotation tools.