Machine Learning and Deep Learning in Computer Vision

Data is the new oil? No: Data is the new soil. ~ David McCandless

⭐ - Recommendations for Beginners.

Awesome Lists

Artificial Intelligence

• ⭐ Awesome Artificial Intelligence - Lightman Wang (General)
• Awesome Artificial Intelligence (AI) - Owain Lewis (General)
• practicalAI - practicalAI
• A list of artificial intelligence tools you can use today - for: 1. Personal use, 2. Business use — Enterprise Intelligence, 2. Business use (cont’d) — Enterprise Functions, and 3. Industry specific businesses
• FirmAI - ML and DS Applications in Industry | ML and DS Applications in Business | ML and DS Applications in Asset Management | ML and DS Applications in Financial

Machine Learning

• ⭐ Machine Learning Mastery - Jason Brownlee (General)
• ⭐ Homemade Machine Learning - Oleksii Trekhleb (Tutorial)
• 2020 Machine Learning Roadmap (Roadmap)
• Python Machine Learning Jupyter Notebooks (Tutorial)
• Machine Learning Mindset (Roadmap)
• ⭐ Awesome Machine Learning - Joseph Misiti
• 3D Machine Learning - Yuxuan (Tim) Zhang
• Machine Learning Interviews from FAAG, Snapchat, LinkedIn - Khang Pham
• Others:
  • mlcourse.ai - Yorko
  • machine learning examples - lazyprogrammer
  • ML From Scratch - eriklindernoren

Deep Learning

• ⭐ Awesome Deep Learning - Christos Christofidis (General)
• Awesome AutoDL - D-X-Y (General)
• ⭐ Deep Learning Papers Reading Roadmap - Flood Sung (Roadmap)
• Awesome Deep Learning Resources - Guillaume Chevalier (General)
• ⭐ Deep Learning Drizzle - Mario (Lecturers)
• Deep Learning with Python Notebooks (Tutorial)
• Awesome Deep Learning for Video Analysis - Huaizheng (General)
• Awesome 3D Point Cloud Analysis - Yongcheng (Roadmap)
• Awesome Tiny Object Detection (General)
• Edge Detection:
  • Awesome Edge Detection Papers - Haoran MO
  • Awesome Background Subtraction - Murari Mandal
  • Awesome Semantic Segmentation - mrgloom
• Object Detection and Tracking:
  • ⭐ Deep Learning Object Detection - Lee hoseong (Roadmap)
  • Deep Learning for Tracking and Detection - Abhineet Singh (Roadmap)
  • Anomaly Detection Resources - Yue Zhao (General)
  • Awesome Anomaly Detection - Lee hoseong (General)

Computer Vision

• ⭐ Awesome Computer Vision - Jia-Bin Huang (General)
• Awesome Deep Vision - Jiwon Kim (General)
• ⭐ Learn OpenCV - Satya Mallick (Tutorial)

Production

• Papers and blogs by organizations sharing their work on data science & machine learning in production: applied-ml - eugeneyan
• Deep-Learning-in-Production - ahkarami: share some useful notes and references about deploying deep learning-based models in production.
• Awesome MLOps: A curated list of references for MLOps.
• ⭐ Awesome MLOps: A curated list of awesome MLOps tools.

Compilers

• Awesome machine learning for compilers and program optimisation: zwang4

Concepts

Mathematics Concepts

• ProofWiki (proofwiki.org): Web
• Book of Proof (Richard Hammack, 2018, 3rd Ed.): Book | Web
• Book of Proofs (bookofproofs.org): Web

Machine Learning Concepts

• ⭐ Pengenalan Pembelajaran Mesin dan Deep Learning (J.W.G. Putra, 2019): Book | GitHub | Web
• Machine Learning Probabilistic Prespective (K.P. Murphy, 2012. The MIT Press): Book | GitHub | Solution | Web
• Pattern Recognition and Machine Learning (C.M. Bishop. 2006. Springer): Book | GitHub | Web
• Mathematics for Machine Learning (M.P. Deisenroth. 2020. Cambridge University Press) Web | Book update. Book printed

Deep Learning Concepts

• Principles of Artificial Neural Networks (Daniel Graupe, 2013): Book
• Principles of Neurocomputing for Science and Engineering (Fredric M. Ham, 2001): Book
• Neural Networks and Deep Learning (M. Nielsen, 2018): Book | GitHub | Web
• ⭐ Neural Networks and Deep Learning (C.C. Aggarwal, 2018. Springer): Book | Web | Slide
• ⭐ Deep Learning (I. Goodfellow, Y. Bengio, & A. Courville. 2016. The MIT Press): Book | GitHub | Web
• Math and Architectures of Deep Learning (K. Chaudhury . 2020. MEAP): Book

Computer Vision Concepts

• ⭐ Computer Vision: Models, Learning, and Inference (Simon J.D. Prince 2012. Cambridge University Pres): Web | Book | GitHub | Matlab Code
• Computer Vision: Algorithms and Application (R. Szeliski 2010. Springer): Book | GitHub | Web

All with Python

Basic Python Books

• CheatSheet > Comprehensive Python Cheatsheet
• ⭐ Python 3 Object-oriented Programming (D. Phillips. 2015. O'Reilly Media): Book | GitHub | Web
• ⭐ Learning Python Design Patterns (G. Zlobin. 2013. Packt): Book | GitHub
• Mastering Python Design Patterns (S. Kasampalis & K. Ayeva. 2018. Packt): Book | GitHub
• ⭐ Clean Code in Python (M. Anaya. 2018. Packt): Book | GitHub
• A collection of design patterns/idioms in Python (Sakis Kasampalis. GitHub): GitHub

Machine Learning with Python

• ⭐ Introduction to Machine Learning with Python (A.C. Muler & S. Guido. 2017. O'Reilly Media): Book | GitHub | Web
• Practical Machine Learning with Python (D. Sarkar, R. Bali, and T. Sharma. 2018. Apress): Book | GitHub
• Machine Learning Applications Using Python (P. Mathur. 2019. Apress): Book | GitHub

Deep Learning with Python

• ⭐ Deep Learning with Applications Using Python (N.K. Manaswi, 2018. Apress): Book | GitHub
• ⭐ Dive into Deep Learning - NumPy/MXNet and PyTorch implementations (Aston Zhang, 2020): Book | GitHub
  • Dive into Deep Learning Compiler (Aston Zhang, 2020): Book | GitHub
• ⭐ Deep Learning with PyTorch (Eli Stevens, 2020. MEAP): Book

Computer Vision with Python

• ⭐ Computer Vision with Python 3 (S. Kapur, 2017. Packt): Book | GitHub
• Programming Computer Vision with Python: Tools And Algorithms For Analyzing Images (Jan Erik Solem, 2012. O'Reilly): Book
• Modern Computer Vision with PyTorch (V Kishore Ayyadevara, 2020. Packt): Book | GitHub

All with C++

Basic C++ Books

• C++ Core Guidelines (a collaborative effort led by Bjarne Stroustrup, much like the C++ language itself): Web | GitHub
• CheatSheet > C++ Cheatsheet | A cheatsheet of modern C++ language and library features | awesome-cpp1 | awesome-cpp2
• cppreference.com > Website
• Matplot++: A C++ Graphics Library for Data Visualization: GitHub
• Programming: Principles and Practice Using C++ (B. Stroustrup. 2008. Addison-Wesley Professional): Book
• The C++ Programming Language (B. Stroustrup. 2013. Addison-Wesley Professional): Book
• Modern C++ Tutorial: C++11/14/17/20 On the Fly (O. Changkun. 2020. ): Web | Book | GitHub

Machine Learning with C++

• Hands-On Machine Learning with C++ (K. Kolodiazhnyi, 2020-05. Packt): Book | GitHub

Deep Learning with C++

• C++ Implementation of PyTorch Tutorials for Everyone: GitHub
• LibtorchTutorials: This is a code repository for pytorch c++ (or libtorch) tutorial. GitHub
  • LibtorchDetection: C++ trainable detection library based on libtorch (or pytorch c++). Yolov4 tiny provided now.
  • LibtorchSegmentation: A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.

Image Processing & Computer Vision with C++

• Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library: Book | GitHub
• The CImg Library is a small and open-source C++ toolkit for image processing: Web

ML Design Patterns & Clean Code Books

• Machine Learning Design Patterns (V. Lakshmanan, S. Robinson, M. Munn. 2020. O'Reilly): Book | GitHub
• Clean Machine Learning Code (M. Taifi, 2020. Leanpub): Book | Course

ML DevOps Books

• Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow (Hannes Hapke, 2020. O'Reilly): Book
• Introducing MLOps: How to Scale Machine Learning in the Enterprise (Mark Treveil. 2020. O'Reilly): Book
• Designing Machine Learning Systems (C. Huyen, 2022. O'Reilly): Book | GitHub

Deep Learning Frameworks

• Deep Learning with Keras (S. Pal & A. Gulli, 2017. Packt): Book and Code

TensorFlow Frameworks

• Project Templates

  • ⭐ Python: Tensorflow-Project-Template - MrGemy95
  • C++: Bla-bla.

• Awesome Lists

  • Awesome TensorFlow - jtoy
  • Awesome TensorFlow 2 - Amin-Tgz

• TensorFlow Books: jtoy/awesome-tensorflow#books | Amin-Tgz/awesome-tensorflow-2#books

  • Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow (Sebastian Raschka, 2017. Packt): Book | GitHub
  • Deep Learning with Python (François Chollet, 2017. Manning): Book | GitHub
  • Deep Learning with TensorFlow (G. Zaccone & Md.R. Karim, 2018. Packt): Book, Code, and GitHub
  • Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition (Antonio Gulli, 2019. Packt): Book | GitHub
  • Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras (Benjamin Planche, 2019. Packt): Book | GitHub
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Aurélien Géron, 2019. O'Reilly): Book | GitHub
  • Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow (Anirudh Koul, 2019. O'Reilly): Book | GitHub
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Aurélien Géron, 2019. O'Reilly). Book | GitHub

• TensorFlow Lite Books: margaretmz/awesome-tensorflow-lite#books

  • TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers (Pete Warden, 2020-01. O'Reilly Media): Book
  • Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS (Anubhav Singh, Rimjhim Bhadani, 2020. Packt): Book

PyTorch Frameworks

• Project Templates

  • ⭐ Python victoresque - pytorch-template
  • Python moemen95 - Pytorch-Project-Template
  • Python L1aoXingyu - Deep-Learning-Project-Template
  • ⭐ C++ kigster - CMake C++ Project Template

• Awesome Lists

  • Awesome Pytorch List - bharathgs (Framework)
  • ⭐ PyTorch Tutorial - Yunjey Choi (Python Tutorial)
  • PyTorch Beginner - liaoxingyu (Python Tutorial)
  • ⭐ C++ Implementation of PyTorch Tutorials for Everyone - prabhuomkar (C++ Tutorial)
  • ⭐ Libtorch Tutorials: This is a code repository for pytorch c++ (or libtorch) tutorial: LibtorchDetection and LibtorchSegmentation.
  • The Incredible PyTorch - ritchieng: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. (Lists)

• PyTorch Books: rickiepark/awesome-pytorch#books

  • Deep Learning with PyTorch (V. Subramanian, 2018. Packt): Book and GitHub
  • Deep Learning with PyTorch 1.0 (S. Yogesh K, 2019. Packt): Book and Code
  • Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications (Ian Pointer, 2019. O'Reilly): Book
  • PyTorch Recipes: A Problem-Solution Approach (Pradeepta Mishra, 2019. Apress): Book
  • PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily (Sherin Thomas, 2019. Packt): Book
  • ⭐ Deep Learning with PyTorch (Eli Stevens, 2020. MEAP): Book and Code
  • Modern Computer Vision with PyTorch (V.K. Ayyadevara & Y. Reddy, 2020. Packt): Book and Code
  • Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD (J. Howard, 2020. O'Reilly): Book
  • PyTorch Computer Vision Cookbook: Over 70 recipes to master the art of computer vision with deep learning and PyTorch 1.x (Michael Avendi, 2020. Packt): Book

• MLOps for PyTorch

  • (AutoML) AutoPyTorch - Automatic architecture search and hyperparameter optimization for PyTorch.
  • (CI/CD for Machine Learning) CML - Open-source library for implementing CI/CD in machine learning projects.
  • (Hyperparameter Tuning) Talos - Hyperparameter Optimization for TensorFlow, Keras and PyTorch.
  • (Model Interpretability) Captum - Model interpretability and understanding library for PyTorch.
  • (Model Serving) TorchServe - A flexible and easy to use tool for serving PyTorch models.
  • (Optimization Tools) Horovod - Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

Network Programming

• ⭐ Foundations of Python Network Programming (Brandon Rhodes. 2014. Apress): Book | GitHub
• C++ Network Programming, Volume I: Mastering Complexity with ACE and Patterns (Douglas Schmidt. 2001. Addison-Wesley Professional): Book
• C++ Network Programming, Volume 2: Systematic Reuse with ACE and Frameworks (Douglas Schmidt. 2002. Addison-Wesley Professional): Book

Courses

Machine Learning

• ⭐ Belajar Machine Learning Lengkap Dari Nol Banget sampai Practical - WiraD.K. Putra (2020): YouTube | GitHub
• Standford Machine Learning - Standford by Andrew Ng (2008): YoutTube
• Caltech Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014): Web
• Neural networks - University De Sherbrooke by Hugo Larochelle (2013): YouTube | Web

Deep Learning

• ⭐ Deep Learning Drizzle - Mario (2021): Website | GitHub
• Carnegie Mellon University Deep Learning - CMU: YouTube | Web
• Deeplearning.ai Neural Networks and Deep Learning - Deeplearning.ai by Andrew Ng in YouTube (2010-2014): YouTube
• Standford Neural Networks and Deep Learning - Standford by Fei-Fei Li: YouTube: 2017
• MIT Deep Learning - MIT by Lex Fridman: GitHub | YouTube
• Stanford Deep Learning - Stanford by Andrew Ng: Homepage | Web | Coursera | GitHub
• Deep Neural Networks with PyTorch - IBM by Joseph Santarcangelo: coursera
• Deep Learning with PyTorch - by sentdex: YouTube
• Computer Vision - Univ. Central Florida by Mubarak Shah YouTube

TinyML

• CS249r: Tiny Machine Learning (TinyML) - Harvard by Vijay Janapa Reddi: sites.google.com | YouTube | edx| GitHub
• Introduction to Embedded Machine Learning - Edge Impulse by Shawn Hymel: coursera
• Embedded and Distributed AI - Jonkoping University, Sweden by Beril Sirmacek: YouTube

MLOps

• Machine Learning Engineering for Production MLOps - by Andrew Ng (2021): Coursera

Research Groups

Universities

• Standford Univ - Machine Learning Group (Prof. Andrew Ng)
• Standford Univ - Vision and Learning Lab (Prof. Fei-Fei Li)
• Univ of Montreal - Mila (Prof. Yoshua Bengio)
• New York Univ - CILVR Lab (Prof. Yann LeCun)
• Univ of Toronto - Machine Learning (Prof. Geoffrey Hinton)
• Barkeley Univ - Artificial Intelligence Research (BAIR) Lab (Prof. Trevor Darrell)
• MIT - Deep Learning (Lex Fridman)

Communities

• ⭐ Q-engineering: Computer vision, Machine learning, Applied mathematics. GitHub
  • PyTorch-Raspberry-Pi-64-OS
  • PyTorch-Jetson-Nano
  • TensorFlow-Raspberry-Pi-32-bit
  • TensorFlow-Raspberry-Pi_64-bit
  • TensorFlow-JetsonNano
• ⭐ HUAWEI Noah's Ark Lab: Working with and contributing to the open source community in data mining, artificial intelligence, and related fields.
  • CV-Backbones: CV backbones including GhostNet, TinyNet and TNT.
  • Pretrained-Language-Model: Pretrained language model and its related optimization techniques.
• ⭐ MIT HAN Lab: Accelerating Deep Learning Computing. Website
  • Tiny Machine Learning: Our projects are covered by: MIT News, WIRED, Morning Brew, Stacey on IoT, Analytics Insight, Techable. Web.
  • once-for-all: [ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment.
  • proxylessnas: [ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.
• ⭐ TinyML - Harvard University
  • tinyMLx - colabs: This repository holds the Google Colabs for the EdX TinyML Specialization.
  • tinyMLx - courseware: In this repository you will find TinyML course syllabi, assignments/labs, code walkthroughs, links to student projects, and lecture videos (where applicable).
  • arduino-library: Harvard_TinyMLx Arduino Library.
• NVIDIA Corporation
  • TRTorch: PyTorch/TorchScript compiler for NVIDIA GPUs using TensorRT.
  • apex: A PyTorch Extension: Tools for easy mixed precision and distributed training in PyTorch.
  • DeepLearningExamples: provides State-of-the-Art Deep Learning examples that are easy to train and deploy, achieving the best reproducible accuracy and performance with NVIDIA CUDA-X software stack running on NVIDIA Volta, Turing and Ampere GPUs.
  • libcudacxx: The C++ Standard Library for your entire system.
• NVIDIA-AI-IOT
  • torch2trt: An easy to use PyTorch to TensorRT converter.
  • tf_trt_models: TensorFlow models accelerated with NVIDIA TensorRT.
  • my-jetson-nano-baseboard: An open source Jetson Nano baseboard and tools to design your own.
• ⭐ OpenMMLab: mmcv - OpenMMLab Computer Vision Foundation.
  • mmclassification: OpenMMLab Image Classification Toolbox and Benchmark
  • mmdetection: OpenMMLab Detection Toolbox and Benchmark.
  • mmsegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark.
  • mmtracking: OpenMMLab Video Perception Toolbox. It supports Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Detection (VID) with a unified framework.
  • mmdetection3d: OpenMMLab's next-generation platform for general 3D object detection.
• Open Neural Network Exchange: ONNX is an open ecosystem for interoperable AI models. It's a community project: we welcome your contributions!
  • onnx: Open standard for machine learning interoperability.
  • onnx-tutorial: Tutorials for creating and using ONNX models.
  • onnx-models: A collection of pre-trained, state-of-the-art models in the ONNX format.
  • tensorflow-onnx: Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX.
  • onnx-tensorrt: ONNX-TensorRT: TensorRT backend for ONNX.
• Cloud-CV: Building platforms for reproducible AI research.
  • EvalAI: Evaluating state of the art in AI.
  • Fabrik: Collaboratively build, visualize, and design neural nets in browser.
  • Origami: Origami: Artificial Intelligence as a Service.
• Iterative: Developer Tools for Machine Learning.
  • dvc: Data Version Control | Git for Data & Models | ML Experiments Management.
  • cml: Continuous Machine Learning | CI/CD for ML.
• ⭐ Machine Learning Tooling - Open-source machine learning tooling to boost your productivity
  • ml-workspace: All-in-one web-based IDE specialized for machine learning and data science.
  • ml-hub: Multi-user development platform for machine learning teams. Simple to setup within minutes.
  • ⭐ best-of-ml-python: A ranked list of awesome machine learning Python libraries.
  • ⭐ best-of-web-python: A ranked list of awesome python libraries for web development.
  • opyrator: Turns your machine learning code into microservices with web API, interactive GUI, and more.
• Megvii - BaseDetection.
  • YOLOX: is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported.
  • cvpods: All-in-one Toolbox for Computer Vision Research.
• AMAI GmbH: AI-Expert-Roadmap: Roadmap to becoming an Artificial Intelligence Expert in 2021.
• Machine Learning Tokyo: AI_Curriculum: Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford, MIT, UC Berkeley.
• Distributed (Deep) Machine Learning Community: xgboost
• ⭐ EthicalML: The Institute for Ethical Machine Learning - The Institute for Ethical Machine Learning is a think-tank that brings together with technology leaders, policymakers & academics to develop standards for ML.
  • awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning.
  • awesome-artificial-intelligence-guidelines: This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.
• ⭐ Hugging Face: The AI community building the future. Website
  • accelerate: A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
  • knockknock: Knock Knock: Get notified when your training ends with only two additional lines of code.
  • datasets: The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools.
  • transformers: Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

Corporations

• ⭐ Tesla AI: GitHub Tesla AI
• Brain Team - Google AI: TensorFlow, GitHub Google AI Research
• Facebook AI: PyTorch, GitHub Facebook Research
• Microsoft AI: Microsoft Cognitive Toolkit (CNTK), GitHub Microsoft AI
• Amazon AI: Alexa
• Apple AI
• Alibaba AI: GitHub Alibaba AI
• IBM AI
• Nvidia AI: GitHub Nvidia AI
• Tencent AI: GitHub Tencent AI
• Open AI: GitHub Open AI

Ph.D. in Machine Learning

• Machine Learning - Carnegie Mellon University
• EECS - University of California — Berkeley
• Computer Science - Stanford University
• EECS - Massachusetts Institute of Technology
• Computer Science - Cornell University

Products

• Self-driving Car: Tesla | Waymo | Lyft | Argo | Voyage | Aurora | cruise |
• Industrial Autonomy & Robotics: bostondynamics | Anki | Mov.ai
• AI: Ultralytics LLC | FirmAI | deepdetect.com

AI Start-Up in Indonesia

• Institutions: ai-innovation.id | Strategi Nasional Kecerdasan Artifisial (KA)
• ChatBot: kata.ai > github.com/kata-ai & medium.com/kata-engineering | prosa.ai > medium.com/@prosa.ai | bahasa.ai > github.com/bahasa-ai & medium.com/bahasa-ai | aichat.id | konvergen.ai > github.com/konvergen & medium.com/konvergen
• Vision: nodeflux.io > github.com/nodefluxio & medium.com/nodeflux & retailmatix - nodeflux.io | delligence.ai | grit.id > github.com/grit-id | riset.ai | jagooo.id
• Data Analytics: eureka.ai | kepingai.com
• Annotation Service: acquaire - nodeflux.io
• Communities: Indonesia AI Society | atapdata.ai | coleaves.ai | jakartamachinelearning | datascienceID | tau-dataID | aidi.id | idbigdata

Datasets

cvpapers.com | wikipedia.org | datasetlist.com | deeplearning.net | datahub.io | towardsai.net | medium-towards-artificial-intelligence

• MNIST Dataset - New York University by Yann LeCun (1998): Raw
• Open Images dataset - Web
• YouTube: YouTube-BoundingBoxes Dataset - E. Real, et. al. | YouTube-8M Dataset - S. Abu-El-Haija, et. al. (2017) | YouTube-VOS Dataset - Ning Xu, et. al. (2018)
• H3D Dataset - Honda by Abhishek Patil et. al. (2019): Paper | Web
• BLVD Dataset - Xian Jiaotong University by Jianru Xue, et. al. (2019): Paper | GitHub
• Awesome Vehicle Dataset: manfreddiaz | hunjung-lim | AmiTitus

Vehicle Classification

• Vehicle image database - Universidad Politécnica de Madrid (UPM) by J. Arróspide (2012) - 3425 images of vehicle rears: Web

Object Detection & Recognition

• CIFAR10 [10] - University of Toronto by Alex Krizhevsky (2009): Raw (10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck) | pdf
• PASCAL VOC [20] - M. Everingham (2012): Raw (20 classes: person: person; animal:bird, cat, cow, dog, horse, sheep; vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train; indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor) | pdf
• COCO [80] - COCO Consortium by Tsung-Yi Lin, et. al. (2015): Web | Download (80 classes: person & accessory, animal, vehicle, aoutdoor objects, sports, kitchenware, food, furniture, appliance, electronics, and indoor objects) | pdf
• CIFAR100 [100] - University of Toronto by Alex Krizhevsky (2009): Raw (100 classes: aquatic mammals: beaver, dolphin, otter, seal, whale; fish: aquarium fish, flatfish, ray, shark, trout, flowers: orchids, poppies, roses, sunflowers, tulips; food containers: bottles, bowls, cans, cups, plates; fruit and vegetables: apples, mushrooms, oranges, pears, sweet peppers; household electrical devices: clock, computer keyboard, lamp, telephone, television; household furniture: bed, chair, couch, table, wardrobe; insects: bee, beetle, butterfly, caterpillar, cockroach; large carnivores: bear, leopard, lion, tiger, wolf; large man-made outdoor things: bridge, castle, house, road, skyscraper; large natural outdoor scenes: cloud, forest, mountain, plain, sea; large omnivores and herbivores: camel, cattle, chimpanzee, elephant, kangaroo; medium-sized mammals: fox, porcupine, possum, raccoon, skunk; non-insect invertebrates: crab, lobster, snail, spider, worm; people: baby, boy, girl, man, woman; reptiles: crocodile, dinosaur, lizard, snake, turtle; small mammals: hamster, mouse, rabbit, shrew, squirrel; trees: maple, oak, palm, pine, willow; vehicles 1: bicycle, bus, motorcycle, pickup truck, train; vehicles 2: lawn-mower, rocket, streetcar, tank, tractor) | pdf
• ImageNet [10,000] Stanford University by Olga Russakovsky (2012) - Raw | pdf

Object Tracking

• KITTI [2]: Raw(2 classes: car & pedestrian) | pdf
• LaSOT [85]: A High-quality Large-scale Single Object TrackingBenchmark - Stony Brook University by Heng Fan (2020): Raw (85 classes) | pdf
• MOT16: A Benchmark for Multi-Object Tracking - Univ. of Adelaide by A. Milan, et. al. (2016)]: Raw | pdf
• TAO [833]: A Large-Scale Benchmark for Tracking Any Object - Carnegie Mellon University by Achal Dave (2020): Raw (833 classes) | pdf

Monocular 3D Object Detection

• KITTI Dataset - University of Tübingen by Andreas Geiger (2012): Raw | Object 2D | Object 3D | Bird's Eye View (8 classes: car, van, truck, pedestrian, person_sitting, cyclist, tram, and misc or don’t care)
• Boxy Dataset - bosch-ai by Karsten Behrendt (2019): Web | 2D Box | 3D Box | Realtime | Paper (1 classes: freeways {passenger cars, trucks, campers, boats, car carriers, construction equipment, and motorcycles}, heavy traffic, traffic jams)
• nuScenes - nuTonomy by Holger Caesar (2019-03) The nuScenes dataset is a large-scale autonomous driving dataset: Link | Toolbox | Paper (23 classes | 19 detection: animal, debris, pushable, bicycle, ambulance, police, barrier, bicycle, bus, car, construction vehicle, motorcycle, pedestrian, personal mobility, stroller, wheelchair, traffic cone, trailer, truck)
• Cityscapes3D - Mercedes-Benz AG by Nils Gählert (2020-06), Dataset and Benchmark for Monocular 3D Object Detection: Link | Toolbox | Paper (8 classes: car, truck, bus, on rails, motorcycle, bicycle, caravan, and trailer)

Hardware

edge-ai - crespum

Edge Hardware

• ⭐ Jetson Nano Dev Board - brings accelerated AI performance to the Edge in a power-efficient and compact form factor: Website | GitHub
• ⭐ Google Coral Dev Board - is a complete toolkit to build products with local AI. Our on-device inferencing capabilities allow you to build products that are efficient, private, fast and offline: Website | GitHub
• Intel Movidius Neural Compute Sticks - enables rapid prototyping, validation and deployment of Deep Neural Network (DNN) inference applications at the edge. : Website | GitHub
• ARM microNPU - Processors designed to accelerate ML inference (being the first one the Ethos-U55): Website
• Espressif ESP32-S3 - SoC similar to the well-known ESP32 with support for AI acceleration (among many other interesting differences): Website
• ⭐ RaspberryPi/Arduino/STM32 + Edge Impulse - Enabling developers to create the next generation of intelligent device solutions through embedded Machine Learning: Website | GitHub
• OpenMV - A camera that runs with MicroPython on ARM Cortex M6/M7 and great support for computer vision algorithms. Now with support for Tensorflow Lite too.
• JeVois - A TensorFlow-enabled camera module.
• Maxim MAX78000 - SoC based on a Cortex-M4 that includes a CNN accelerator.
• Beagleboard BeagleV - Open Source RISC-V-based Linux board that includes a Neural Network Engine.

Processor: The Deep Learning Compiler: A Comprehensive Survey - arXiv '20

• Tensor Processing Unit (TPU) by Google: Wiki
• Neural Processing Unit (NPU) by MobilePhone Company: Wiki
• Vision Processing Unit (VPU) by NEC & Intel: Wiki
• Intelligence Processing Unit (IPU) by Graphcore: GitHub
• Machine Learning Unit (MLU) by Cambricon: GitHub

Deep Learning for Embedded (IOT) & Mobile Devices

Frameworks

Embedded and mobile deep learning - csarron | Awesome Mobile Machine Learning - fritzlabs | Awesome Edge Machine Learning - Bisonai | edge-ai - crespum | AI-performance - embedded-ai.bench

• ⭐ TensorFlow Lite: TensorFlow Lite is an open source deep learning framework for on-device inference.
• The Arm's ComputeLibrary framework: ComputeLibrary is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.
• The Alibaba's MNN framework: MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba.
• ⭐ The Tencent's ncnn framework: ncnn is a high-performance neural network inference framework optimized for the mobile platform.
• The Baidu's Paddle Lite framework: Paddle Lite is multi-platform high performance deep learning inference engine.
• The XiaoMi's Mace framework: MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
• The Apple's CoreML framework: CoreML is integrate machine learning models into your app.
• The Microsoft's ELL framework: ELL allows you to design and deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers, like Raspberry Pi, Arduino, and micro:bit.
• ⭐ PyTorch Mobile: PyTorch Mobile is a new framework for helping mobile developers and machine learning engineers embed PyTorch ML models on-device.
• dabnn - JDAI Computer Vision: dabnn is an accelerated binary neural networks inference framework for mobile platform.
• opencv-mobile: opencv-mobile is open source computer vision library that was designed to be cross-platform. The minimal opencv for Android, iOS and ARM Linux.
• DeepLearningKit: DeepLearningKit is Open Source Deep Learning Framework for Apple's iOS, OS X and tvOS.
• Tengine - OAID: Tengine is a lite, high performance, modular inference engine for embedded device.
• Bender: Bender is easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.
• uTensor - AI inference library based on mbed (an RTOS for ARM chipsets) and TensorFlow.
• CMSIS NN - A collection of efficient neural network kernels developed to maximize the performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
• ARM Compute Library - Set of optimized functions for image processing, computer vision, and machine learning.
• Qualcomm Neural Processing SDK for AI - Libraries to developers run NN models on Snapdragon mobile platforms taking advantage of the CPU, GPU and/or DSP.
• X-CUBE-AI - Toolkit for generating NN optimiezed for STM32 MCUs.
• Neural Network on Microcontroller (NNoM) - Higher-level layer-based Neural Network library specifically for microcontrollers. Support for CMSIS-NN.
• nncase - Open deep learning compiler stack for Kendryte K210 AI accelerator.
• deepC - Deep learning compiler and inference framework targeted to embedded platform.
• uTVM - MicroTVM is an open source tool to optimize tensor programs.
• Edge Impulse - Interactive platform to generate models that can run in microcontrollers. They are also quite active on social netwoks talking about recent news on EdgeAI/TinyML.

Books

1. Mobile Edge Artificial Intelligence [Elsevier '21]

Tools

Production

• docker.com: build and ship apps.
• onnx.ai: open format built to represent machine learning models.
• mlflow.org: an open source platform for the machine learning lifecycle.
• cortex.dev: the open source stack for machine learning engineering.
• mlperf.org: Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.
• grpc: A high performance, open source, general-purpose RPC framework.
• gpustat: A simple command-line utility for querying and monitoring GPU status.
• jetson-stats: Simple package for monitoring and control your NVIDIA Jetson [Xavier NX, Nano, AGX Xavier, TX1, TX2].
• nnabla-ext-cuda: A CUDA Extension of Neural Network Libraries.

Training Model

• DIGITS: DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. The currently supported frameworks are: Caffe, Torch, and Tensorflow.
• Optuna: A hyperparameter optimization framework.
• Determined: Deep Learning Training Platform.
• cuDF: GPU DataFrame Library.
• DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
• comet.ml: track, compare, explain and optimize experiments and models.
• dvc: Data Version Control | Git for Data & Models.
• Weights & Biases: Experiment tracking, model and dataset versioning, hyperparameter optimization.
• modelzoo.co: Discover open source deep learning code and pretrained models.

Visualization: Architecture

• ⭐ Netron: a viewer for neural network, deep learning and machine learning models.
• ⭐ NN-SVG: Publication-ready NN-architecture schematics.
• ⭐ ennui: Working on a drag-and-drop neural network visualizer (and more). Here's an example of a visualization for a LeNet-like architecture.
• TensorSpace: TensorSpace is a neural network 3D visualization framework built by TensorFlow.js, Three.js and Tween.js. TensorSpace provides Layer APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. By applying TensorSpace API, it is more intuitive to visualize and understand any pre-trained models built by TensorFlow, Keras, TensorFlow.js, etc.
• netscope: A web-based tool for visualizing and analyzing convolutional neural network architectures (or technically, any directed acyclic graph).
• playground: Deep playground is an interactive visualization of neural networks, written in TypeScript using d3.js.
• PerceptiLabs: a dataflow driven, visual API for TensorFlow that enables data scientists to work more efficiently with machine learning models and to gain more insight into their models.
• conv: 3D visualization of convolutional neural network.
• PyTorchViz: A small package to create visualizations of PyTorch execution graphs and traces.
• PlotNeuralNet: Latex code for making neural networks diagrams.
• ml-visuals: ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
• traingenerator: A web app to generate template code for machine learning.
• nni: an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
• nn-visualizer: Interactive 3D Neural Network Visualizer.

Dashboard

• wave - Realtime Web Apps and Dashboards for Python and R.
• mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
• Flask JSONDash - Build complex dashboards without any front-end code.
• thingsboard - Open-source IoT Platform - Device management, data collection, processing and visualization.
• freeboard - A damn-sexy, open source real-time dashboard builder for IOT and other web mashups.
• Augmented Reality (VR) & Virtual Reality (VR) Dashboard
  • ViroReact: AR and VR using React Native.
  • Awesome lists: Domeee/awesome-augmented-reality, dharmeshkakadia/awesome-AR, Vytek/VR-Awesome, mnrmja007/awesome-virtual-reality

Interested Research

• Deep Learning Models - A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks: GitHub
• Hyperparameter Optimization of Machine Learning Algorithms - Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear): GitHub
• FairMOT - A simple baseline for one-shot multi-object tracking: GitHub
• Norfair - is a customizable lightweight Python library for real-time 2D object tracking: GitHub
• Transformer: Awesome Visual-Transformer | pytorch2libtorch | Fast Transformers

Autonomous Vehicles

• Awesome Autonomous Vehicles - manfreddiaz: GitHub
• Autoware - Integrated open-source software for urban autonomous driving: Web | GitHub
• CARLA Simulator - Open-source simulator for autonomous driving research: GitHub
• Self-DrivingToy Car - experiencor: GitHub
• openpilot: is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 85 supported car makes and models.

Benchmark

benchmarks.ai | dawn.cs.stanford.edu | mlperf.org | MobilePhone - ai-benchmark.com | GitHub > deep-learning-benchmark - u39kun, DeepBench - baidu-research

• MLPerf is a trademark of MLCommon
  • Training High Performance Computing
  • Training normal
  • Data Center
  • Edge
  • Tiny
  • Mobile
• GPUs Benchmark videocardbenchmark.net | gpu.userbenchmark.com
  • Deep Learning GPU Benchmarks
  • Video Card Benchmark
  • CPU Benchmark
• Tools
  • Jetson Nano: Deep Learning Inference Benchmarks
  • 2017-02 - Benchmarking State-of-the-Art Deep Learning Software Tools
  • 2019-07 - Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
  • 2019-04-20 - Google Coral Edge TPU Board Vs NVIDIA Jetson Nano Dev board — Hardware Comparison
• Object Classification
  • MNIST
  • CIFAR-10
  • CIFAR-100
• Object Detection
  • PASCAL VOC
  • COCO
  • KITTI
• Multi-Object Tracking
  • MOT16
  • MOT20
  • KITTI
• NLP
  • IndoNLU Benchmark

Create Datasets

• Get Image from Sources
  • Creating a dataset using an API with Python
  • How to create a dataset in Google Colab for your Machine Learning projects
  • How to (quickly) build a deep learning image dataset
  • Tips for building large image datasets
• Dataset Tools
  • Dataset Search - google
  • awesome dataset tools - a curated list of awesome dataset tools

Journals, Magazines, and People

Journals

• AI: Artificial Intelligence (Q1) | Journal of Artificial Intelligence Research (Q1) | Artificial Intelligence Review (Q1)
• Machine Learning: Journal of Machine Learning Research (Q1) | Machine Learning (Q1) | Foundations and Trends in Machine Learning (Q1)
• Computer Vision: Image and Vision Computing (Q1) | Computer Vision and Image Understanding (Q1) | International Journal of Computer Vision (Q1)

Magazines: towardsdatascience | paperswithcode | distill | xenonstack | awesomeopensource.com | emerge-ai.com

• AI: towards-artificial-intelligence - AI | towardsdatascience - AI | AI - ID
• Machine Learning: towardsdatascience - ML | ML - ID | jakartamachinelearning
• Deep Learning: paperswithcode - NLP | deeplearningweekly.com
• Computer Vision: paperswithcode - CV

People

• AI: Ayu Purwarianti, Dr (Computer Science, Toyohashi University of Technology) | Igi Ardiyanto, Dr (Robotics, Toyohashi University of Technology) | Muhammad Ghifary, PhD (AI, Victoria University of Wellington)
• Machine Learning: Dwi H. Widyantoro, Dr (Machine Learning, Texas A&M University)

Podcast

• Indonesian Tech/Dev: Ceritanya Developer Podcast - Riza Fahmi | Kode Nol - deep tech foundation
• Indonesian StartUp: Startup Studio Indonesia - Startup Studio Indonesia | The Spectrum Talks - Anggriawan Sugianto | Ngobrolin Startup & Teknologi - Imre Nagi | Startup Hour by StartupIndonesia: StartupIndonesia | #NgobrolinStartup - Dailysocial Podcast
• Data Science: Towards Data Science - The TDS team | DataPods - Data Science Indonesia | Data Talks - KBR Prime x Algoritma
• AI: AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion - Cognilytica | Practical AI: Machine Learning & Data Science | Lex Fridman Podcast - Lex Fridman
• IoT: IoT For All Podcast - IoT For All | IOTALK - IOTIZEN

Conferences & Competitions for Image Processing & Computer Vision: guide2research.com | openaccess.thecvf.com

• ⭐ CVPR: IEEE/CVF Conference on Computer Vision and Pattern Recognition. Paper list: https://openaccess.thecvf.com/CVPR2020
• ⭐ LPCV: Low Power Computer Vision Competition. Website: https://lpcv.ai/
• ICCV: IEEE/CVF International Conference on Computer Vision. Paper list: https://openaccess.thecvf.com/ICCV2019
• ECCV: European Conference on Computer Vision. Paper list: https://link.springer.com/conference/eccv
• WACV: Workshop on Applications of Computer Vision. Paper list: https://openaccess.thecvf.com/WACV2020
• 3DV: International Conference on 3D Vision. Website http://3dv2020.dgcv.nii.ac.jp/index.html
• ACCV: Asian Conference on Computer Vision (ACCV). Website: http://accv2020.kyoto/
• AAAI: Association for the Advancement of Artificial Intelligence. Website: https://aaai.org/Conferences/conferences.php