The paper introduces CSPNet (Cross Stage Partial Network), a new backbone architecture designed to enhance the learning capability of Convolutional Neural Networks (CNNs) while reducing computational costs. CSPNet addresses the issue of redundant gradient information within network optimization, which leads to inefficient optimization and costly inference computations. By integrating feature maps from both the beginning and end of a network stage, CSPNet reduces computational load by 20% while maintaining or improving accuracy on the ImageNet dataset. It also outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. CSPNet is applicable to architectures like ResNet, ResNeXt, and DenseNet, and it can be easily implemented. The paper also introduces the Exact Fusion Model (EFM), which further reduces memory traffic and computational bottlenecks, making the model suitable for deployment on CPUs and mobile GPUs. Experimental results demonstrate that CSPNet significantly improves accuracy and inference speed, outperforming competitors in real-time object detection tasks.The paper introduces CSPNet (Cross Stage Partial Network), a new backbone architecture designed to enhance the learning capability of Convolutional Neural Networks (CNNs) while reducing computational costs. CSPNet addresses the issue of redundant gradient information within network optimization, which leads to inefficient optimization and costly inference computations. By integrating feature maps from both the beginning and end of a network stage, CSPNet reduces computational load by 20% while maintaining or improving accuracy on the ImageNet dataset. It also outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. CSPNet is applicable to architectures like ResNet, ResNeXt, and DenseNet, and it can be easily implemented. The paper also introduces the Exact Fusion Model (EFM), which further reduces memory traffic and computational bottlenecks, making the model suitable for deployment on CPUs and mobile GPUs. Experimental results demonstrate that CSPNet significantly improves accuracy and inference speed, outperforming competitors in real-time object detection tasks.