ImageNet provided a massive labeled dataset of over 1 million images across 1,000 categories, while AlexNet demonstrated that deep convolutional neural networks could dramatically outperform traditional computer vision methods on this challenging benchmark.

AlexNet's victory in the 2012 ImageNet Large Scale Visual Recognition Challenge marked a turning point, achieving a top-5 error rate of 15.3% compared to 26.2% from the second-place traditional method. This breakthrough proved that deep learning could scale to real-world problems with sufficient data and compute power.

The success launched the current era of deep learning, inspiring rapid adoption across industries and establishing convolutional neural networks as the foundation for modern computer vision systems.