**VGG** is a classical convolutional neural network architecture. It was based on an analysis of how to increase the depth of such networks. The network utilises small 3 x 3 filters. Otherwise the network is characterized by its simplicity: the only other components being pooling layers and a fully connected layer.

Image: [Davi Frossard](https://www.cs.toronto.edu/frossard/post/vgg16/)

**SNet** is a convolutional neural network architecture and object detection backbone used for the [ThunderNet](https://paperswithcode.com/method/thundernet) two-stage object detector. SNet uses ShuffleNetV2 basic blocks but replaces all 3×3 depthwise convolutions with 5×5 depthwise convolutions.

SNet

ThunderNet: Towards Real-time Generic Object Detection

Very Deep Convolutional Networks for Large-Scale Image Recognition

**PointNet** provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. It directly takes point clouds as input and outputs either class labels for the entire input or per point segment/part labels for each point of the input.

Source: [Qi et al.](https://arxiv.org/pdf/1612.00593v2.pdf)

Image source: [Qi et al.](https://arxiv.org/pdf/1612.00593v2.pdf)

Source	Very Deep Convolutional Networks for Large-Scale Image Recognition
Year	2000
Data Source	Papers with Code archive (CC BY-SA)