CSPNet vs. CSP: Understanding the Key Differences in Neural Network Architectures

CSPNet and CSP (Cross Stage Partial Network) are two distinct neural network architectures.

CSP is a lightweight network architecture designed to address issues like gradient vanishing and overfitting prevalent in deep neural networks. It achieves this by dividing the network into two stages - a front-end and a back-end - and introducing a cross-stage partial connection between them. This structure enhances network stability and reliability.

CSPNet builds upon CSP by incorporating additional features, such as attention mechanisms and multi-scale feature fusion. These enhancements contribute to improved performance and accuracy.

Therefore, CSPNet offers superior performance and stronger feature extraction capabilities compared to CSP.