  Neural networks have enabled state-of-the-art approaches to achieve
incredible results on computer vision tasks such as object detection. However,
such success greatly relies on costly computation resources, which hinders
people with cheap devices from appreciating the advanced technology. In this
paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem
that previous works require heavy inference computations from the network
architecture perspective. We attribute the problem to the duplicate gradient
information within network optimization. The proposed networks respect the
variability of the gradients by integrating feature maps from the beginning and
the end of a network stage, which, in our experiments, reduces computations by
20% with equivalent or even superior accuracy on the ImageNet dataset, and
significantly outperforms state-of-the-art approaches in terms of AP50 on the
MS COCO object detection dataset. The CSPNet is easy to implement and general
enough to cope with architectures based on ResNet, ResNeXt, and DenseNet.
Source code is at https://github.com/WongKinYiu/CrossStagePartialNetworks.
