An Industrial-grade Solution for Convolutional Neural Network Optimization and Deployment

The deployment of deep learning models into practical production applications has become increasingly important with the rapid development of deep learning in theoretical research. Currently, the deployment of deep learning models faces numerous challenges due to the increasingly large scale and computational requirements of these models, along with the limited storage and computing resources of mobile devices. This paper proposes a high-performance and versatile solution to address the challenges of practical model deployment. This study significantly enhances model inference speed by employing techniques such as DepGraph model pruning, operator fusion, and the NCNN inference framework while reducing the model size and storage overhead.