CSPDarknet

CSPDarknet is a convolutional neural network backbone designed for real-time object detection. It is a backbone architecture that applies Cross-Stage Partial (CSP) connections to the Darknet family, resulting in a model with competitive accuracy and lower computational cost than Darknet-53. It was popularized as the backbone for the YOLOv4 detector.

The core idea is to split the feature map into two parts; one part is processed through several bottleneck residual blocks, while the other part passes through unchanged. After processing, the two parts are concatenated. This reduces redundancy in gradient flow and feature reuse, enabling more efficient training and inference. CSPDarknet typically uses standard convolutional layers with 3x3 and 1x1 kernels, residual connections, and a progressive downsampling schedule to generate multi-scale feature maps used by the detector's neck (PANet) and head.

The canonical version is CSPDarknet-53, named for its depth. Lightweight variants exist for faster inference on limited hardware. It is commonly paired with a feature pyramid neck and path-aggregation network in YOLOv4-style detectors. Activation and specific block designs may vary by implementation; some configurations employ Mish or Leaky ReLU activations.

CSPDarknet contributed to improvements in accuracy-to-computation trade-offs for real-time detection on standard benchmarks and enabled more efficient training than earlier backbones. It remains a reference backbone in discussions of CSPNet-based designs and in earlier generation YOLO models.