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YOLOv4-large is an open-source implementation of the Scaled-YOLOv4 object detection architecture, designed to improve both the accuracy and scalability of real-time computer vision models. The project provides a PyTorch implementation of the Scaled-YOLOv4 framework, which extends the original YOLOv4 architecture using Cross Stage Partial (CSP) networks and new scaling techniques. Unlike earlier object detection systems that only scale depth or width, this architecture scales multiple aspects of the neural network including structure, resolution, and channel configuration. This scaling strategy enables the model to adapt to different hardware environments while maintaining a strong balance between speed and detection accuracy. The repository includes multiple model variants such as YOLOv4-tiny, YOLOv4-CSP, and large-scale configurations designed for high-performance detection tasks.
Features
- Implementation of Scaled-YOLOv4 object detection models using PyTorch
- Architecture scaling across depth, width, resolution, and network structure
- Multiple model variants including YOLOv4-tiny and YOLOv4-large
- High-performance real-time object detection for images and video
- Training scripts and configuration files for custom dataset training
- Support for GPU-accelerated deep learning workflows
Project Samples
