dreamplayerzhang,github

tls

tmf

TMF is a framework for modeling stream processing system programs with a dataflow model. Modeling such programs with dataflow allows them to be parallelized and distributed. TMF implements some OpenCV actors.

tnn

TNN: developed by Tencent Youtu Lab and Guangying Lab, a lightweight and high-performance deep learning framework for mobile inference. 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. TNN：由腾讯优图实验室和光影实验室协同打造，移动端高性能、轻量级推理框架，同时拥有跨平台、高性能、模型压缩、代码裁剪等众多突出优势。TNN框架在原有Rapidnet、ncnn框架的基础上进一步加强了移动端设备的支持以及性能优化，同时也借鉴了业界主流开源框架高性能和良好拓展性的优点。目前TNN已经在手Q、微视、P图等应用中落地，欢迎大家参与协同共建，促进TNN推理框架进一步完善。

transferlearning

Resources and codes about transfer learning and domain adaptation--迁移学习

transwarp

A header-only C++ library for task concurrency

traveling-salesman

Traveling Salesman Problem C++ Implementation

trending-deep-learning

Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day.

trilinos

Primary repository for the Trilinos Project

tscancode

A static code analyzer for C++, C#, Lua

turborle

Fastest Run Length Encoding

tutorials

Here, we will be showcasing our seminar series “CPP for Image Processing and Machine Learning” including presentations and code examples. There are image processing and machine learning libraries out there which use C++ as a base and have become industry standards (ITK for medical imaging, OpenCV for computer vision and machine learning, Eigen for linear algebra, Shogun for machine learning). The documentation provided with these packages, though extensive, assume a certain level of experience with C++. Our tutorials are intended for those people who have basic understanding of medical image processing and machine learning but who are just starting to get their toes wet with C++ (and possibly have prior experience with Python or MATLAB). Here we will be focusing on how someone with a good theoretical background in image processing and machine learning can quickly prototype algorithms using CPP and extend them to create meaningful software packages.