AlexNet

In adoption terms, AlexNet today is better understood as a historical landmark and teaching baseline than as a mainstream vision model still actively adopted in production. The evidence strongly supports its status as a turning point in 2012 ImageNet history: multiple retrospectives, explainers, and long-form Zhihu posts frame it that way. But there is no evidence here that new projects commonly choose it today. The heat proof mainly comes from reposts, nostalgia threads, and list-like discussions on X, which show continued attention rather than present-day usefulness.

Functionally, AlexNet is not a text-to-image model and not a ready-to-call vision API. A better analogy is a classic CNN case study or a bridge between early models like LeNet and later families like ResNet. The stronger usefulness evidence in this set comes from paper explainers, a compact PyTorch reimplementation tutorial, and reporting that the original code was publicly surfaced much later. Those sources support the view that AlexNet is still useful for learning large kernels, ReLU, dropout, and early multi-GPU training design, as well as for coursework and paper reproduction.