Classic CNNs: LeNet, AlexNet, VGG, ResNet, EfficientNet
How did neural networks go from reading handwritten digits to beating humans at naming anything in a photo? This is the story of five architectures — LeNet-5, AlexNet, VGG, ResNet, EfficientNet — each of which won a round of the ImageNet competition by changing one big thing. We cover what each changed, why it mattered, and which design lessons still apply in 2024-2026.
01.The Problem: Machines Used to Be Bad at Seeing
Ask a five-year-old: "Is there a dog in this picture?" Instant answer.
Ask a computer in 1995 and you get a shrug. Pixels arrive as millions of numbers; edges, textures, and objects have to be discovered from them.
For a long time the best approach was hand-crafted features — human experts writing rules for what patterns matter (for example SIFT: manually designed "what local blob/edge shapes look like" descriptors). It worked, barely.
So the question becomes
Can a machine learn what to look for directly from images, instead of us coding the rules?
That is what convolutional neural networks (CNNs — networks that slide small learned filters over an image) are for. And this topic is their Olympic history: five architectures, each in turn the best "seer" on Earth, each winning by changing one big thing.
Why should a beginner care about old models?
Because every modern vision model — every detector, segmenter, and vision Transformer — is built from parts these five invented or popularized.
Every design choice you see today traces back to one of these papers.
The ImageNet architecture timeline 🏆
The ImageNet architecture timeline 🏆
Each generation won by making the network deeper and more parameter-efficient, until residual connections (2015) removed the depth barrier and compound scaling (2019) optimized all dimensions jointly.
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