之江天枢 / model_script_library Python

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在这个版本中，我们提供了几个基于OneFlow的计算机视觉应用，包括: - Re-ID - DCGAN - Pix2Pix - SRGAN - Face Segmentation - Action Recognition - Restrack - TTSR - AP3D - GhostNet - HRNet - LeNet - Scnet - poseNet - TCA

原始仓库地址：https://gitee.com/zhijiangtianshu/Model_Script_Library.git

之江天枢 / OneFlow_RepVGG Python

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OneFlow 搭建RepVGG

原始仓库地址：https://gitee.com/zhijiangtianshu/OneFlow_RepVGG.git

之江天枢 / cycle-gan Python

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使用 Oneflow 框架实现 CycleGan。

原始仓库地址：https://gitee.com/zhijiangtianshu/cycle-gan.git

opencv_3rdparty C

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# OpenCV: Open Source Computer Vision Library This repository contains 3rdparty libraries used by OpenCV. ### OpenCV Resources OpenCV repository: https://github.com/opencv/opencv Homepage: https://opencv.org Docs: https://docs.opencv.org Q&A forum: https://answers.opencv.org Issue tracking: https://github.com/opencv/opencv/issues

原始仓库地址：https://github.com/opencv/opencv_3rdparty.git

prp Python

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This is the implementation of "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning" in IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2020)

原始仓库地址：https://github.com/yuanyao366/prp.git

北京米唐科技有限公司 / PowerRoom C#

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一款10kV变电站电力知识XR软件 1 基于Unity+Microsoft Mixed Reality。 2 针对10kV小区、学校配电室中KYN28-12高压柜与XGN15-12环网柜开发：设备结构认知、电力操作模拟、冲击电压发生器实验、特斯拉高压线圈实验。 3 为沈阳职业技术学院电力工程专业学生提供无风险的高压电力知识学习。

legonet Python

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A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).

原始仓库地址：https://github.com/huawei-noah/legonet.git

gan-pruning Python

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A Pytorch implementation of "Co-Evolutionary Compression for Unpaired Image Translation" (ICCV 2019).

原始仓库地址：https://github.com/huawei-noah/gan-pruning.git

bht-arima Python

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Code for paper: Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting (AAAI-20)

原始仓库地址：https://github.com/huawei-noah/bht-arima.git

data-efficient-model-compression Python

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This repo is the Pytorch implementation of Data-Efficient Model Compression.

原始仓库地址：https://github.com/huawei-noah/data-efficient-model-compression.git