CNN-based model more lightweight? Just take the smaller version of that model, right? Like with ResNet, for instance, if ResNet-152 feels too heavy, why not just use ResNet-101? Or in the case of DenseNet, why not go with DenseNet-121 rather than DenseNet-169? — Yes, that’s true, but you would have to sacrifice some accuracy for that. Basically, if you want a lighter model then you should expect your accuracy to drop as well.  Now, what if I told you about a model that’s more lightweight than its base but can still compete on accuracy? Meet CSPNet (Cross Stage Partial Network). You’ll be surprised that it can effectively reduce computational complexity while maintaining high accuracy — no tradeoff! In this article we are going to talk about the CSPNet architecture, including how it works and how to implement it from scratch. A Brief History of CSPNet CSPNet was first introduced in a paper titled “ CSPNet: A New Backbone That Can Enhance Learning Capability of CNN ” written by Wang et al.…