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hd-celeba-cropper

To obtain high resolution face images from CelebA

jekyll-lin-theme

A slick minimalist theme for Jekyll built with lin.css

jekyll-theme-hamilton

A minimal and beautiful Jekyll theme best for writing and note-taking.

lcamera

A camera app using the new camera2 API in Android Lollipop

libcamera-apps

lightmeter

Android App to measure luminance of a room.

lightsensor

Android application to measure the brightness the phone is exposed to, usually to be able to adjust the screen's brightness using Kotlin Programming Language.

machine-learning-specialization

Machine Learning Specialization work from UW on Coursera

matraw

Read and Process Camera Raw Data with MATLAB

microsoft-integration-and-azure-stencils-pack-for-visio

Microsoft Integration, Azure, Power Platform, Office 365 and much more Stencils Pack it’s a Visio package that contains fully resizable Visio shapes (symbols/icons) that will help you to visually represent On-premise, Cloud or Hybrid Integration and Enterprise architectures scenarios (BizTalk Server, API Management, Logic Apps, Service Bus, Event Hub…), solutions diagrams and features or systems that use Microsoft Azure and related cloud and on-premises technologies in Visio 2016/2013

mimo_ofdm

《MIMO-OFDM无线通信技术及MATLAB实现》随书源码

ms_ssim_pytorch

A fast ssim & ms-ssim implement code with pytorch jit.

mvs

Multi-View Stereo based on deep learning. | Learning notes, codes and more

neural_image_simulator

ofdm

A MATLAB program to help understand OFDM.

ofdm-papr

concept of PAPR of OFDM and how to decrease PAPR

overlaypermission

悬浮窗权限兼容库

particletextview

一个用粒子动画显示文字的 Android 自定义 View

picamera

A pure Python interface to the Raspberry Pi camera module

pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

pytorch-grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

pytorch-msssim

Fast and differentiable MS-SSIM and SSIM for pytorch.

pytorch_retinaface

Retinaface get 80.99% in widerface hard val using mobilenet0.25.

react-native-display-cutout

robust_physical_perturbations

Public release of code for Robust Physical-World Attacks on Deep Learning Visual Classification (Eykholt et al., CVPR 2018)

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

sensor-fusion-demo

＃Android的传感器融合演示[！[Build Status]（https://www.bitrise.io/app/46b5cf7adea1286f.svg?token=MZUhPFZvIBiaTSEinY9zUQ&branch=master）]（https://www.bitrise.io/app/46b5cf7adea1286f ）[！[Build Status]（https://travis-ci.org/apacha/sensor-fusion-demo.svg?branch=master）]（https://travis-ci.org/apacha/sensor-fusion-演示）[！[文档状态]（https://readthedocs.org/projects/sensor-fusion-demo/badge/?version=latest）]（http://sensor-fusion-demo.readthedocs.io/en/最新/？徽章=最新）本应用程序演示了各种传感器和传感器融合的功能。陀螺仪，加速度计和罗盘的数据以不同的方式组合，结果显示为可以通过旋转设备旋转的立方体。阅读完整的文档[这里]（http://sensor-fusion-demo.readthedocs.io）。该应用中的主要新颖之处在于虚拟传感器的融合：**改进的方位传感器1 **和**改进的方位传感器2 **将Android旋转矢量与虚拟陀螺仪传感器融合，以获得以前未知的稳定性的姿态估计和精度。除了这两个传感器，以下传感器可用于比较： - 改进的方向传感器1（Android旋转矢量和校准陀螺仪的传感器融合 - 不太稳定但更准确） - 改进的方向传感器2（Android旋转矢量的传感器融合和校准陀螺仪 - 更稳定但不太准确） - Android旋转矢量（加速度计+陀螺仪+罗盘）的卡尔曼滤波器融合 - 校准陀螺仪（加速度计+陀螺仪+指南针卡尔曼滤波器的独立结果） - 重力+指南针 - 加速度计+指南针本应用被开发用于展示为[我的论文“传感器融合为移动设备上强大的户外增强现实跟踪”开发的传感器融合方法]（http://my-it.at/media/MasterThesis-Pacha.pdf）在[人类接口技术实验室新西兰]（http://www.hitlabnz.org）。##构建和安装此项目是基于Gradle的Android Studio项目。如果您只想尝试一下，它也会在[Google Play商店]（https://play.google.com/store/apps/details?id=org.hitlabnz.sensor_fusion_demo）中发布。##贡献1.分叉2.创建您的功能分支（`git checkout -b my-new-feature`）3.提交你的更改（`git commit -am'添加一些功能'）4.推到分支（`git push origin my-new-feature`）5.根据MIT许可证创建新的Pull Request ##许可证。版权所有，2017年由[亚历山大·帕查]（http://alexanderpacha.com）和[人力资源技术实验室新西兰]（http://www.hitlabnz.org）。特此授权任何获得本软件和相关文档文件（“软件”）副本的人免费处理本软件，包括但不限于使用，复制，修改，合并的权利，发布，分发，再许可和/或出售本软件的副本，并允许提供本软件的人员遵守以下条件：上述版权声明和本许可声明应包含在所有副本中或软件的主要部分。该软件“按原样”提供，不附带任何明示或暗示的保证，包括但不限于适销性，适用于特定用途和不侵权的保证。在任何情况下，作者或版权所有者均不对任何索赔，损害或其他责任负责，无论是否因与本软件或本软件的使用或其他交易相关的任何合同，侵权行为或其他方面的行为软件。此应用程序还使用Android开放源代码项目的部分，它们是根据[Apache许可证版本2.0]（http://www.apache.org/licenses/LICENSE-2.0）许可的。##数据隐私声明本应用程序不存储或传输任何数据。

smartphone-sensors-app

An android app that showcases raw data from various sensors in a smartphone

smoothing

Provable adversarial robustness at ImageNet scale

solarprogressview

类似于太阳花形状的自定义进度条