This comes from a question that “how can we improve CNN performance without exploding compute and parameter costs ?”
VGGNet revolutionized CNN design by proving that depth + simplicity beats complexity. The key insight was replacing large filters (7x7) with stacked small filters (3x3) to get the same receptive field but with more non-linearities and fewer parameters
Historical Significance:
- ImageNet 2014 runner-up with 145,000+ citations
- Influenced every major CNN architecture that followed (ResNet, DenseNet, etc.)
- Still used today as a transfer learning backbone
The uniform 3x3 convolution approach with gradual channel progression (64→128→256→512) created a clean, modular architecture that became the blueprint for modern CNNs
- Deepen networks to capture more complex features
- Use uniform, simple building blocks (only 3×3 filters)
- Replace larger filters (5×5, 7×7) with multiple stacked 3×3 filters
Transfer Learning Insights: When using pretrained VGG with frozen layers
- Pretrained conv layers already work well on natural images
- Higher validation accuracy in early epochs indicates good generalization from ImageNet features
- Not overfitting - features align well with validation set