Youngsoo Lee (leeys888)

leeys888

Geek Repo

Company:@naver

Location:Seoul, South Korea

Home Page:www.linkedin.com/in/leeyoungsoo15

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Youngsoo Lee's repositories

ncnn-portrait-segmentation

⚡️Real-time portrait segmentation

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fast-neural-style-tensorflow

A tensorflow implementation for fast neural style!

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AES

C++ AES implementation

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Efficient-Segmentation-Networks

Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)

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gitignore

A collection of useful .gitignore templates

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insightface

Face Analysis Project on MXNet and PyTorch

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iOSDropDown

Drop Down Menu for iOS With Search And Other Awesome Customisation

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janus-gateway

Janus WebRTC Server

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MNN

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba

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ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

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pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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pytorch-template

PyTorch deep learning projects made easy.

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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。

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