Training deep learning models from scratch is often impractical due to massive data requirements and long training times. This article explains why these challenges exist and how pretrained models and transfer learning enable faster, more efficient model development with limited data and resources.

Pretrained models in CNNs allow us to reuse knowledge learned from large datasets like ImageNet to build accurate computer vision systems with less data, time, and computational cost. This article explains pretrained models, ImageNet, ILSVRC, AlexNet, and the evolution of modern CNN architectures.