Over the past decades, CNN architectures have evolved dramatically, each breakthrough building upon previous innovations. Let's explore this fascinating journey through the most influential architectures that shaped modern computer vision.
💡 Each architecture addressed specific limitations of its predecessors, pushing the boundaries of what's possible in image recognition and beyond.
CNN Architecture Evolution
1998
LeNet-5
The grandfather of modern CNNs, designed for digit recognition
~60K params7 layers
2012
AlexNet
Sparked the deep learning revolution with ImageNet victory
60M params8 layers
2014
VGGNet
Showed that deeper networks with smaller filters work better
138M params16-19 layers
2014
GoogLeNet/Inception
Introduced inception modules for efficient multi-scale processing
7M params22 layers
2015
ResNet
Revolutionary skip connections enabled very deep networks
25.6M params50-152 layers
2017
DenseNet
Connected every layer to every other layer for maximum information flow
8M params121+ layers
2017
MobileNet
Efficient architecture for mobile and embedded vision applications
4.2M params28 layers
2019
EfficientNet
Balanced network scaling for optimal accuracy vs efficiency