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

Abstract: 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.

• The paper introduces CSPNet, a novel CNN backbone that partitions feature maps to enhance gradient diversity and reduce redundant computation.
• Experiments show that CSPNet cuts computational load by up to 20% while maintaining or improving accuracy on ImageNet and MS COCO datasets.
• CSPNet integrates effectively with architectures like ResNet and DenseNet, offering a promising solution for resource-constrained environments.

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

The "Cross Stage Partial Network (CSPNet)" paper introduces a novel architectural modification for Convolutional Neural Networks (CNNs) aiming to improve computational efficiency without compromising on accuracy. The paper identifies redundant gradient information within existing CNN architectures, which leads to inefficiency in terms of computational demands and optimization processes. To address this, CSPNet integrates feature maps from the beginning and end of a network stage to improve gradient variability and reduce computation.

Key Contributions

1. Introduction of CSPNet:

The primary innovation of the paper is the CSPNet architecture, which is versatile and can be adapted for use with well-established architectures such as ResNet, ResNeXt, and DenseNet. CSPNet operates by partitioning the feature map of the base layer into two parts and then merging them through a cross-stage hierarchy, thus promoting greater gradient diversity and reducing redundancy in information flow.

2. Significant Computational Reductions:

Experimental results highlight that CSPNet can reduce computational loads by 20% while either maintaining or improving accuracy on the ImageNet dataset. Notably, on the MS COCO object detection dataset, CSPNet exhibits superior performance in terms of AP$_{50}$.

3. Applications to Various Architectures:

CSPNet showcases adaptability by effectively integrating with ResNet, ResNeXt, and DenseNet architectures. When applied to ResNet and ResNeXt, for instance, CSPNet retains the original performance benefits while decreasing computation costs significantly.

Methodological Details

DenseNet and CSPDenseNet:

The detailed structure of a DenseNet stage involves dense blocks and transition layers where outputs from each layer are concatenated to form inputs for the following layers. This strategy enhances feature reuse but also results in repetitive learning of gradient information. CSPDenseNet counters this by splitting the input feature map into two parts, ensuring one part goes through a dense block while the other bypasses it, thereby merging them via partial transition layers. This truncation of gradient flow ensures a richer gradient combination.

Partial Transition Layer:

The partial transition layer employs a hierarchical feature fusion process, effectively preventing redundant gradient information. This feature uses empirical strategies to retain important gradient flow while discarding duplicated information, demonstrated through a series of comparative experiments.

Experimental Validation

ImageNet Classification:

On the ImageNet dataset, CSPNet demonstrates its efficiency by reducing computational efforts by up to 19% in DenseNet architectures. Particularly, CSPPeleeNet achieves notable improvements in both computation reduction (13%) and accuracy enhancement (0.8%).

MS COCO Object Detection:

Using the MS COCO dataset, CSPNet coupled with Exact Fusion Model (EFM) achieves exceptional results. It yields higher AP and AP$_{50}$ values compared to contemporary models while maintaining competitive inference speeds.

Implications and Future Directions

The innovative approach of CSPNet holds significant implications for both theoretical research and practical applications. By reducing computational needs and enhancing gradient flow, CSPNet can facilitate the deployment of state-of-the-art deep learning models in resource-constrained environments such as mobile and edge computing devices. This can lead to more accessible AI technology, ensuring robust performance on low-power devices.

Future research could explore further optimizations in CSPNet for additional architectures and application scenarios. Additionally, integrating CSPNet with newer hardware accelerators could unlock even further efficiency gains, paving the way for widespread adoption in real-world applications.

By addressing the fundamental issues of gradient redundancy and computational bottlenecks, CSPNet significantly contributes to the ongoing efforts to create more efficient and scalable neural network architectures. This work stands as a critical step towards optimizing deep learning models for broader and more impactful everyday use.