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A structured light scanner
阿布量化交易系统(股票,期权,期货,比特币,机器学习) 基于python的开源量化交易,量化投资架构
Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research
An open autonomous driving platform
Apollo(阿波罗)是携程框架部门研发的分布式配置中心,能够集中化管理应用不同环境、不同集群的配置,配置修改后能够实时推送到应用端,并且具备规范的权限、流程治理等特性,适用于微服务配置管理场景。
Scenarios, tutorials and demos for Autonomous Driving
A TensorFlow implementation of this Nvidia paper: https://arxiv.org/pdf/1604.07316.pdf with some changes
Open-Source To Self-Driving.
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
Scripts to use BDD Dataset
Third Project of the Udacity Self-Driving Car Nanodegree Program
Caffe: a fast open framework for deep learning.
Open-source simulator for autonomous driving research.
Euro Truck Simulator 2 autonomous driving solution
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
Deep Learning Book Chinese Translation
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet代码实现改为PyTorch实现。
The Web framework for perfectionists with deadlines.
Experiment files for the paper "Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?", available here: http://arxiv.org/abs/1510.02969
Kaggle Python docker image
documentations related HiKey, HiKey960 and HiKey970
Dual Path Networks
Dubbo is a high-performance, java based, open source RPC framework
Deprecated, please go to next generation Ultra-Low Power RISC-V Core https://github.com/riscv-mcu/e203_hbirdv2
The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
Read-only mirror of https://gerrit.hyperledger.org/r/#/admin/projects/fabric
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.