Top 23 C++ Deep Learning Projects
An Open Source Machine Learning Framework for EveryoneProject mention: Google stock | reddit.com/r/stocks | 2021-08-01
Google leads in every layer of the AI stack. Lowest layer is silicon and their TPUs have been setting records with both training and inference at scale. "Google's TPU Pods are Breaking Records — And We Aren't Surprised" https://blog.bitvore.com/googles-tpu-pods-are-breaking-benchmark-records Next layer up above the silicon is basically the AI operating system. The most popular today is TensorFlow which is from Google. The second most popular is Pytorch from Facebook. TensorFlow now has over 150K stars on Github. https://github.com/tensorflow/tensorflow One of the most popular projects on Github. The next layer above the framework is the algorithms. The best way to measure success with algorithms is the papers accepted at the canonical AI organization, NeurlIPS. In 2018, 2019, 2020 and sure will be the same in 2021 has seen Google having the most papers accepted by a large margin. https://imgur.com/ZWeeqUg Next layer is data. Which is probably where people most think of Google and Facebook. But Google has the more valuable data. Facebook data is what you want people to think of you. Versus Google has more truthful information. There is probably no more valuable data than search queries you make. It is this very unusual window into who you are. But with Google there is then all their other services. YouTube has the video data. Google Maps the location data. Google Photos is the biggest repository of photos. The list goes on and on. The next layer up is the applications. Search is the biggest AI/ML driven application in the world. Search penetrates 99% of the population of the US and then on top of that Google now has over 92% share. https://gs.statcounter.com/search-engine-market-share Why Google search share is increasing while competitors like Bing are declining is because of the Google superior AI. Google is now getting 2/3 of queries ending without needing a click. That is up from 50% the year before. "In 2020, Two Thirds of Google Searches Ended Without a Click" https://sparktoro.com/blog/in-2020-two-thirds-of-google-searches-ended-without-a-click/ BTW, it is clear to me the goal with Google search is ultimately AGI. I believe that is exactly how AGI will happen at some point in the future. That might be 20 years off though. Or longer. This is the past in a way. It is really about new things. I am a techie and love technology and by far the most impressive technology thing I have seen is https://www.youtube.com/watch?v=tBJ0GvsQeak&feature=youtu.be The car pulls up completely empty. No way to fake the technology when there is no driver or even a backup driver to take over. But as impressive this is the bigger AI/ML breakthrough with Google is the protein folding. Think about this. Did anyone think 25 years ago that advertising would be completely owned by big tech? Of course not. We are going to see big tech eat up one industry after another and AI is going to be a huge part of that. Take transportation. That will go to automation and that takes AI. Same with health.
Open Source Computer Vision LibraryProject mention: First Python Project Tips | reddit.com/r/learnpython | 2021-07-28
A classmate of mine back in the day made a rig that would automate the deadbolt on their front door. Obviously there are physical security concerns here but it would probably use an embedded board (Arduino or otherwise) and a servo (basis of a lot of robotics). If memory serves, they also tried hooking up a webcam to use OpenCV to do facial recognition as well.
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Tensors and Dynamic neural networks in Python with strong GPU accelerationProject mention: [D] [P] Deep Learning in Rust with GPU on ONNX | reddit.com/r/MachineLearning | 2021-07-30
Hey there, thanks for reading ! So, i have not tried the tch crate. I have heard that libtorch is very heavy? -> https://github.com/pytorch/pytorch/issues/34058 but, I genuinely think that we need as many bindings as possible for rust as package for ml come and goes and onnx may go rogue at some point.
Caffe: a fast open framework for deep learning.Project mention: Can someone please guide me regarding these different face detection models? | reddit.com/r/learnmachinelearning | 2021-05-27
Caffe is a DL framework just like TensorFlow, PyTorch etc. OpenPose is a real-time person detection library, implemented in Caffe and c++. You can find the original paper here and the implementation here.
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimationProject mention: We built a free, open-source markerless motion capture system during the pandemic. This animation was created with 4x $20US webcams and a gaming PC, details in the comments [OC] | reddit.com/r/dataisbeautiful | 2021-07-21
while I congratulate you for putting together a pipeline, I think it has to be said that the basis of this - the very powerful openpose - has a license that is not completely FOSS. It might be open source but is definitely not free as free beer. So if you consider using this for a fancy project (i.e. marketing an app), you have to get in touch with university of california first https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/LICENSE
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.Project mention: Voice search in firefox | reddit.com/r/firefox | 2021-07-28
I think they got rid of the backend to even support it. Mozilla still has the Deepspeech project although it might be slowed down.
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit (by microsoft)
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Cross-platform, customizable ML solutions for live and streaming media.Project mention: New Pi owner - installing and opening packages | reddit.com/r/RASPBERRY_PI_PROJECTS | 2021-07-21
After checking out the samples on mediapipe.dev, take a look at:
A toolkit for making real world machine learning and data analysis applications in C++Project mention: Guidance on getting started with machine learning using C++ | reddit.com/r/learnmachinelearning | 2021-08-01
Still if you want to use c++ then I would recommend dlib library. https://github.com/davisking/dlib
Open-source simulator for autonomous driving research.Project mention: Carla simulator review? | reddit.com/r/EngineeringStudents | 2021-06-30
This is the link of CARLA: https://carla.org/
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in AlibabaProject mention: Newbie having error code of cannot build selected target abi x86 no suitable splits configured | reddit.com/r/AndroidStudio | 2021-04-12
I found a solution on GitHub check your app's build.gradle, defaultConfig section - you need to add x86 to your ndk abiFilters ndk.abiFilters 'armeabi-v7a','arm64-v8a', 'x86' GitHub Hope it will help. You have to find that file and edit it as given here
header only, dependency-free deep learning framework in C++14
A flexible, high-performance serving system for machine learning modelsProject mention: Running concurrent inference processes in Flask or should I use FastAPI? | reddit.com/r/flask | 2021-03-29
Don't roll this yourself. Look at Tensorflow Serving: https://github.com/tensorflow/serving.
ONNX Runtime: cross-platform, high performance ML inferencing and training acceleratorProject mention: [D] Huggingface finally get a stable inference serving env for their 10k+ models | reddit.com/r/MachineLearning | 2021-07-21
Models larger than 2 GB are still not officially supported in ONNX. So if you want to get your large model hosted on AWS efficiently (CPU) you'll still have to do all the quantization (employing the workaround discussed in that issue) and AWS deployment yourself.
Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.Project mention: Jetson Nano | reddit.com/r/JetsonNano | 2021-07-18
Jetson-Inference is another amazing resource to get started on. This will allow you to try out a number of neural networks (classification, detection, and segmentation) all with your own data or with sample images included in the repo.
TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.Project mention: OpenAI's Sam Altman: Artificial Intelligence will generate enough wealth to pay each adult $13,500 a year | reddit.com/r/Futurology | 2021-03-30
Nvidia TensortRT Free
A C++ standalone library for machine learning (by flashlight)Project mention: [D] C++ in Machine Learning. | reddit.com/r/MachineLearning | 2021-04-25
mlpack: a scalable C++ machine learning library --
A distributed graph deep learning framework. (by alibaba)
OneFlow is a performance-centered and open-source deep learning framework.Project mention: A new-generation deep learning framework is launched[Project] | reddit.com/r/MachineLearning | 2021-07-20
This is the first time to introduce our project OneFlow on Reddit, which is a performance-centered and open-source deep learning framework.
The Triton Inference Server provides an optimized cloud and edge inferencing solution. (by triton-inference-server)Project mention: Triton: Open-Source GPU Programming for Neural Networks | news.ycombinator.com | 2021-07-28
Unfortunate name clash with NVIDIAs Triton Inference Server: https://developer.nvidia.com/nvidia-triton-inference-server
oneAPI Deep Neural Network Library (oneDNN)Project mention: Is gpu hardware tied to cpu ISA ? | reddit.com/r/hardware | 2021-01-11
Intel are trying to support their oneAPI compute framework on Arm and IBM POWER and z/Architecture (s390x) but since they ever released only a single discrete GPU with the Xe architecture it's unclear whether they'll support Xe GPU compute on e.g. ARM https://github.com/oneapi-src/oneDNN
Deep Learning API and Server in C++14 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNEProject mention: [P] Benchmarking OpenBLAS on an Apple MacBook M1 | reddit.com/r/MachineLearning | 2020-12-30
Interesting, thanks. Recently benchmarked inference with Vulkan/MoltenVK/NCNN, M1 GPU is roughly 30% faster than M1 CPU, https://github.com/jolibrain/deepdetect/pull/1105 for single batch inference (NCNN does not really support batch size > 1).
What are some of the best open-source Deep Learning projects in C++? This list will help you: