Computer Vision
CNN Architectures — From LeNet to EfficientNet
Convolutional Neural Networks are the foundation of computer vision. This tutorial covers the convolution operation in depth and the evolution from LeNet to modern architectures.
- Convolution is Feature Detection — Local patterns detected with parameter sharing and translation equivariance
- Skip Connections Changed Everything — ResNet enabled training of 100+ layer networks by solving vanishing gradients
- Compound Scaling — EfficientNet uniformly scales width, depth, and resolution for optimal efficiency
CNN Architecture Deep Dive — LeNet to ResNet to EfficientNet
Convolutional Neural Networks are the foundation of computer vision. This tutorial covers the convolution operation in depth and the evolution of CNN architectures.
The Convolution Operation
Padding and Stride
Pooling
CNN Architecture Evolution
LeNet (1998)
AlexNet (2012)
VGGNet (2014)
ResNet (2015)
Inception Module (GoogLeNet)
Depthwise Separable Convolution
EfficientNet: Compound Scaling
Design Principles
Summary
- Convolution detects local patterns with parameter sharing and translation equivariance
- LeNet → AlexNet → VGG: Deeper networks with simple building blocks
- ResNet: Skip connections enable training of 100+ layer networks
- Inception: Multi-scale features with parallel filter branches
- MobileNet/EfficientNet: Depthwise separable convolutions for efficiency
- Compound scaling: Balance depth, width, and resolution for optimal performance
Next: Object Detection