Machine learning

Philosophy

Artificial intelligence

Object detection

Deep learning

Feature (linguistics)

Pattern recognition (psychology)

Feature engineering

Backbone network

Visual arts

Computation

Data mining

Perspective (graphical)

Inference

Computer science

Algorithm

Linguistics

Architecture

Computer network

Object (grammar)

State (computer science)

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 AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> 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.

CSPNet: A New Backbone that can Enhance Learning Capability of CNN