Description
SNet is a convolutional neural network architecture and object detection backbone used for the ThunderNet two-stage object detector. SNet uses ShuffleNetV2 basic blocks but replaces all 3×3 depthwise convolutions with 5×5 depthwise convolutions.
Papers Using This Method
Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets2024-03-28Real Time Egocentric Segmentation for Video-self Avatar in Mixed Reality2022-07-04Rethinking Image Deraining via Rain Streaks and Vapors2020-08-03Egocentric Human Segmentation for Mixed Reality2020-05-25ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices2019-10-01ThunderNet: Towards Real-time Generic Object Detection2019-03-28