**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.

**Monte-Carlo Tree Search** is a planning algorithm that accumulates value estimates obtained from Monte Carlo simulations in order to successively direct simulations towards more highly-rewarded trajectories. We execute MCTS after encountering each new state to select an agent's action for that state: it is executed again to select the action for the next state. Each execution is an iterative process that simulates many trajectories starting from the current state to the terminal state. The core idea is to successively focus multiple simulations starting at the current state by extending the initial portions of trajectories that have received high evaluations from earlier simulations.

Source: Sutton and Barto, Reinforcement Learning (2nd Edition)

Image Credit: [Chaslot et al](https://www.aaai.org/Papers/AIIDE/2008/AIIDE08-036.pdf)

Monte-Carlo Tree Search

SNet

ThunderNet: Towards Real-time Generic Object Detection

**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/)

Source	ThunderNet: Towards Real-time Generic Object Detection
Year	2000
Data Source	Papers with Code archive (CC BY-SA)