Enhancing the Efficiency of Convolutional Neural Networks through Quantization

A new kind of image categorisation technology, Convolutional Neural Networks (CNNs) have shown themselves capable of astounding accuracy across a range of uses. Problems arise, however, when dealing with real-time applications in settings where resources are limited due to their computational complexity and resource needs. An extensive investigation on how to make convolutional neural networks (CNNs) better at picture classification is detailed in this work. We explore several optimization techniques, including network pruning, quantization, and the deployment of lightweight architectures such as MobileNet and SqueezeNet. Additionally, we investigate the impact of advanced training strategies like transfer learning and data augmentation on model performance. We show, by means of comprehensive tests, that our suggested approaches considerably reduce the memory footprint and computational cost of CNNs while preserving or even enhancing classification accuracy. Our findings provide valuable insights for deploying efficient CNN models in practical scenarios, paving the way for more accessible and scalable image classification solutions.