| Architecture |
Year |
Key contribution |
Depth
|
| LeNet-5 |
1998 |
Pioneered CNNs for handwritten digit recognition (MNIST) |
5 layers
|
| AlexNet |
2012 |
Won ImageNet; popularised ReLU, dropout, GPU training |
8 layers
|
| VGGNet |
2014 |
Showed depth matters; used only $ 3 \times 3 $ filters throughout |
16–19 layers
|
| GoogLeNet (Inception) |
2014 |
Introduced inception modules with parallel filter sizes |
22 layers
|
| ResNet |
2015 |
Introduced residual connections enabling very deep networks |
50–152+ layers
|
| DenseNet |
2017 |
Connected each layer to every subsequent layer via dense blocks |
121–264 layers
|
| EfficientNet |
2019 |
Compound scaling of depth, width, and resolution |
Variable
|
