Pinned Repositories
FinalProject-Lost-and-Found
JigenDG
Repository for the CVPR19 oral paper "Domain Generalization by Solving Jigsaw Puzzles"
models
Models and examples built with TensorFlow
TensorRT-YOLOv4
tensorrt5, yolov4, yolov3,yolov3-tniy,yolov3-tniy-prn
ticgit
Git based distributed ticketing system, including a command line client and web viewer
TNN
TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.
bohebuleng's Repositories
bohebuleng/FinalProject-Lost-and-Found
bohebuleng/JigenDG
Repository for the CVPR19 oral paper "Domain Generalization by Solving Jigsaw Puzzles"
bohebuleng/models
Models and examples built with TensorFlow
bohebuleng/TensorRT-YOLOv4
tensorrt5, yolov4, yolov3,yolov3-tniy,yolov3-tniy-prn
bohebuleng/ticgit
Git based distributed ticketing system, including a command line client and web viewer
bohebuleng/TNN
TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.