Collection of links for different specific issues

• Readme template
  https://gist.github.com/PurpleBooth/109311bb0361f32d87a2

• Documentation-handbook
  https://github.com/jamiebuilds/documentation-handbook

• Documenting on github
  https://guides.github.com/features/wikis/

• How to write a good documentation
  https://guides.lib.berkeley.edu/how-to-write-good-documentation
  https://www.sohamkamani.com/blog/how-to-write-good-documentation/

• How to visualize md files offline
  https://github.com/joeyespo/grip

• Auto-Documenting a Python Project Using Sphinx
  https://medium.com/better-programming/auto-documenting-a-python-project-using-sphinx-8878f9ddc6e9

• Clear, Functional C++ Documentation with Sphinx + Breathe + Doxygen + CMake
  https://devblogs.microsoft.com/cppblog/clear-functional-c-documentation-with-sphinx-breathe-doxygen-cmake/

• Aruco
  https://docs.opencv.org/trunk/d5/dae/tutorial_aruco_detection.html
• AprilTag
  https://april.eecs.umich.edu/software/apriltag

• How to write a good review
  https://m.youtube.com/watch?v=W1zPtTt43LI

• How I Read a Paper: Facebook's DETR (Video Tutorial)
  https://www.youtube.com/watch?v=Uumd2zOOz60&app=desktop

• Common indexing and measure in paper
  https://github.com/aditya30394/Person-Re-Identification https://en.wikipedia.org/wiki/Jaccard_index
• Map Averace Prevision (mAP)
  https://medium.com/@jonathan_hui/map-mean-average-precision-for-object-detection-45c121a31173
• Rank
  https://medium.com/analytics-vidhya/ranked-accuracy-11bdaef795e3
• Texute Structure Similarity (SSIM) - Human perspective
  https://ieeexplore.ieee.org/document/1284395
• Texture Peak Signal-to-noise ratio (PSNR) - Pixel differencies
  https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
  https://www.ni.com/ja-jp/innovations/white-papers/11/peak-signal-to-noise-ratio-as-an-image-quality-metric.html
• Learned Perceptual Image Patch Similarity (LPIPS)
  Paper: Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018.
  Code: https://github.com/richzhang/PerceptualSimilarity
• Understanding AUC - ROC Curve
  https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5

• Understanding PyTorch with an example: a step-by-step tutorial
  https://towardsdatascience.com/understanding-pytorch-with-an-example-a-step-by-step-tutorial-81fc5f8c4e8e

• Simple regression: linear regression
  https://medium.com/@benjamin.phillips22/simple-regression-with-neural-networks-in-pytorch-313f06910379

• Graph and irregular structures
  https://github.com/rusty1s/pytorch_geometric

• PyTorch wrapper for ML researchers
  https://github.com/PyTorchLightning/pytorch-lightning

• C++ of pytorch exaples for Deep Learning Researchers
  https://github.com/prabhuomkar/pytorch-cpp

-- Convolution

• How to train neural network
  https://towardsdatascience.com/pytorch-basics-how-to-train-your-neural-net-intro-to-cnn-26a14c2ea29

• Convolution 1D 2D
  https://discuss.pytorch.org/t/understanding-convolution-1d-output-and-input/30764
  https://datascience.stackexchange.com/questions/32455/which-convolution-should-i-use-conv2d-or-conv1d

• How To Train Your Neural Net
  Collection of examples about how to train a neural network.
  https://github.com/theairbend3r/how-to-train-your-neural-net

-- Loss functions

• Loss functions in PyTorch
  https://medium.com/udacity-pytorch-challengers/a-brief-overview-of-loss-functions-in-pytorch-c0ddb78068f7
• Build own loss
  https://discuss.pytorch.org/t/build-your-own-loss-function-in-pytorch/235/3

-- NN Learning Issues

• NN does not learn
  https://stats.stackexchange.com/questions/352036/what-should-i-do-when-my-neural-network-doesnt-learn

• How to Evaluate Efficient Deep Neural Network Approaches @ CVPR 2020
  https://www.youtube.com/watch?v=HTu7RokJsxc&app=desktop

-- Gradient

https://ruder.io/optimizing-gradient-descent/
https://medium.com/syncedreview/iclr-2019-fast-as-adam-good-as-sgd-new-optimizer-has-both-78e37e8f9a34
https://stats.stackexchange.com/questions/316464/how-does-batch-size-affect-convergence-of-sgd-and-why

-- Combine two separate networks

https://groups.google.com/forum/#!topic/torch7/FACB5BKS4_Q
https://discuss.pytorch.org/t/combining-trained-models-in-pytorch/28383

-- Siamese Neural Network

• ADL4CV - Siamese Neural Networks and Similarity Learning
  https://www.youtube.com/watch?v=6e65XfwmIWE&app=desktop

• Collection of models (code/doc) in pytorch. NB: Not all the models run.
  https://github.com/eriklindernoren/PyTorch-GAN

• [CVPR 2020] Image2StyleGAN++: How to Edit the Embedded Images?
  https://www.youtube.com/watch?v=yd5WczbFt68&app=desktop
  Paper: https://arxiv.org/abs/1911.11544v1

• [ICCV 2019 - Best Paper] SinGAN
  code: https://github.com/tamarott/SinGAN
  Paper: https://arxiv.org/pdf/1905.01164.pdf
  Youtube: https://www.youtube.com/watch?v=xk8bWLZk4DU&ab_channel=TamarRottShaham
  Online Description: https://www.youtube.com/watch?v=-f8sz8AExdc&ab_channel=HenryAILabs

-- Features

• CVPR2020 tutorial: Local Features: From SIFT to Differentiable Methods
  https://www.youtube.com/watch?v=ZscK5p9hZBI&app=desktop

-- Geometry

• [CVPR 2020 Oral] High-dimensional Convolutional Neural Networks for Geometric Pattern Recognition
  https://www.youtube.com/watch?v=bsPGPRrAJOY&app=desktop

-- Unsupervised

• Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
  Paper: https://arxiv.org/pdf/1911.11130.pdf
  Code: https://github.com/elliottwu/unsup3d

-- Scene reconstruction

• Neural Radiance Fields for Unconstrained Photo Collections https://nerf-w.github.io/
  Paper: https://arxiv.org/abs/2008.02268

• Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes
  https://www.cs.cornell.edu/~zl548/NSFF/
  Paper: https://arxiv.org/abs/2011.13084
  Code: https://github.com/zhengqili/Neural-Scene-Flow-Fields

• 3D Photography using Context-aware Layered Depth Inpainting
  https://shihmengli.github.io/3D-Photo-Inpainting/
  Paper: https://drive.google.com/file/d/17ki_YAL1k5CaHHP3pIBFWvw-ztF4CCPP/view
  Code:

-- Monocular

• 3D Packing for Self-Supervised Monocular Depth Estimation [CVPR 2020]
  https://www.youtube.com/watch?v=b62iDkLgGSI&app=desktop
  Paper: https://arxiv.org/abs/1905.02693
  Code: https://github.com/tri-ml/packnet-sfm

• 3D Dense reconstruction based on structure from motion algorithms [SIGGRAPH 2020]
  https://roxanneluo.github.io/Consistent-Video-Depth-Estimation/
  Paper: https://arxiv.org/pdf/2004.15021.pdf
  Code: https://github.com/facebookresearch/consistent_depth

-- RGB

• BubbleNets: CVPR 2019 Oral Presentation (Best Paper Award Finalist!)
  https://www.youtube.com/watch?v=XBEMuFVC2lg&app=desktop
  Paper: http://openaccess.thecvf.com/content_CVPR_2019/html/Griffin_BubbleNets_Learning_to_Select_the_Guidance_Frame_in_Video_Object_CVPR_2019_paper.html
  Code: https://github.com/griffbr/BubbleNets

-- Perspective Camera

• Popular object detection frameworks
  https://github.com/open-mmlab/mmdetection
  https://github.com/facebookresearch/detectron2
• Old project useful for practice but no longer mantained
  https://github.com/jwyang/faster-rcnn.pytorch

-- Pytorch Efficient Detection

• EfficientDet
  https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch

-- Small Object Detection

• Awesome Tiny Object Detection
  https://github.com/kuanhungchen/awesome-tiny-object-detection

• small-object-detection Experiments with different models for object detection on the Pascal VOC 2007 dataset.
  https://github.com/fl16180/small-object-detection

-- Keras & Tensorflow

• Mask R-CNN for Object Detection and Segmentation
  https://github.com/KhoronusFork/Mask_RCNN

-- Single Shot

• CVPR 2020: D3S - A Discriminative Single Shot Segmentation Tracker
  https://www.youtube.com/watch?v=E3mN_hCRHu0&app=desktop
  Paper: https://arxiv.org/abs/1911.08862
  Code: https://github.com/alanlukezic/d3s

• Fast Online Object Tracking and Segmentation: A Unifying Approach
  http://www.robots.ox.ac.uk/~qwang/SiamMask/
  Paper: https://arxiv.org/pdf/1812.05050.pdf
  Code: https://github.com/foolwood/SiamMask
  Code Modified: https://github.com/JosieHong/SiamMask_on_Your_Own_Dataset
  Annotation Version: https://github.com/umedalab/siammask_annotation

-- Object Tracking Given Bounding Boxes

• Simple Online and Realtime Tracking (SORT)
  Code: https://github.com/abewley/sort

• Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT)
  Code: https://github.com/abhyantrika/nanonets_object_tracking

• Tracking without bells and whistles
  https://github.com/phil-bergmann/tracking_wo_bnw

• pymot
  This is a python implementation which determines the MOTP and MOTA metrics from a set of ground truth tracks and a set of hypothesis tracks given by the tracker to be evaluated.
  https://github.com/Videmo/pymot

-- Challenge/Benchmark

• Multiple Object Tracking Benchmark
  https://motchallenge.net/

-- Benchmark

• py-motmetrics
  The py-motmetrics library provides a Python implementation of metrics for benchmarking multiple object trackers (MOT).
  https://github.com/cheind/py-motmetrics

-- Monocular

• Human pose estimation
  https://github.com/wangzheallen/awesome-human-pose-estimation

• Pose (papers with code)
  https://paperswithcode.com/task/pose-prediction/latest

• VIBE: Video Inference for Human Body Pose and Shape Estimation (CVPR 2020)
  https://www.youtube.com/watch?v=rIr-nX63dUA&app=desktop
  Paper: https://arxiv.org/abs/1912.05656
  Code: https://github.com/mkocabas/VIBE

• CVPR 2020 Oral: Combining detection and tracking for human pose estimation in videos
  Paper: https://arxiv.org/abs/2003.13743

-- Commercial Projects

• Only web Cam. Real time motion capture. - 3D pose estimation
  https://www.youtube.com/watch?v=o06Zo7ZU4Mo&app=desktop
  Code: https://github.com/digital-standard/ThreeDPoseUnityBarracuda

• AlphaPose
  AlphaPose is an accurate multi-person pose estimator
  https://github.com/MVIG-SJTU/AlphaPose

• Keras Openpose
  keras-openpose-reproduce
  https://github.com/kevinlin311tw/keras-openpose-reproduce

-- Monocular

• VideoPose3D
  3D human pose estimation in video with temporal convolutions and semi-supervised training
  https://github.com/facebookresearch/VideoPose3D

[CVPR-2020] Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
https://www.youtube.com/watch?v=8mgD8JxBOus&app=desktop
Paper: https://arxiv.org/abs/2002.11616
Code: https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020

• Video2X Upscaling software
  Code: https://github.com/k4yt3x/video2x

How to write a just in time (JIT) compiler
https://github.com/spencertipping/jit-tutorial
https://eli.thegreenplace.net/2017/adventures-in-jit-compilation-part-1-an-interpreter/
https://softwareengineering.stackexchange.com/questions/29344/jit-compiler-for-c-c-and-the-likes

How to write a parser
https://stackoverflow.com/questions/1843502/c-create-a-parser
https://unclechromedome.org/c++-tutorials/expression-parser/index.html
https://algoland.wordpress.com/2013/12/29/a-tiny-parser-in-c/
https://www.toptal.com/c-plus-plus/creating-an-expression-parser-in-c-

How to write a scripting language
https://gamedev.stackexchange.com/questions/421/how-do-you-add-a-scripting-language-to-a-game
https://www.flipcode.com/archives/Implementing_A_Scripting_Engine-Part_1_Overview.shtml
http://gameprogrammingpatterns.com/bytecode.html

• CircleCI circleci CI/CD : cycle implementation, cycle deployment Reduce the implementation time
  https://circleci.com/

• COLMAP COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline
  https://colmap.github.io/

• OpenSfM
  OpenSfM is a Structure from Motion library written in Python
  https://github.com/mapillary/OpenSfM

• Few-shot vid2vid Translate a video sequence in another video sequence.
  https://github.com/NVlabs/few-shot-vid2vid

• DAIN
  Video frame interpolation to vary video frame rate.
  Paper: https://sites.google.com/view/wenbobao/dain
  Code: https://github.com/baowenbo/DAIN

• Deep Deterministic Policy Gradient
  Specifically adapted for environments with continuous action spaces.
  https://spinningup.openai.com/en/latest/algorithms/ddpg.html
  Code: https://github.com/cookbenjamin/DDPG

• Visdom
  A flexible tool for creating, organizing, and sharing visualizations of live, rich data.
  https://github.com/facebookresearch/visdom

• JupyterLab
  A web-based user interface for Project Jupyter
  https://jupyterlab.readthedocs.io/en/stable/
• Password/Token issue (forgotten/set new).
  $ jupyter lab password
  jupyter/notebook#2971
  Note: In the case of notebook, please change lab with notebook. The console message informs about the encoded password location.

• Remote Desktop
  AnyDesk. A remote desktop which can be used throw firewall.
  https://anydesk.com/en
• Tunnels to localhost
  https://ngrok.com/
  Read also: https://towardsdatascience.com/how-to-share-your-jupyter-notebook-in-3-lines-of-code-with-ngrok-bfe1495a9c0c

• CS480/680 Lecture 19: Attention and Transformer Networks (!!!)
  https://www.youtube.com/watch?v=OyFJWRnt_AY&t=851s

• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
  https://arxiv.org/abs/2101.03961

• T5: Text-To-Text Transfer Transformer
  https://github.com/google-research/text-to-text-transfer-transformer

• Transformers for Image Recognition at Scale (!!!)
  https://ai.googleblog.com/2020/12/transformers-for-image-recognition-at.html
  https://github.com/google-research/vision_transformer

• Knowledge Distillation (Wiki)
  https://en.wikipedia.org/wiki/Knowledge_distillation

• Knowledge Distillation: A Survey (2020)
  https://arxiv.org/pdf/2006.05525.pdf

• Full implementation pytorch/tensorflow code with videos (youtube).
  https://github.com/aladdinpersson/Machine-Learning-Collection

• C++ implementation
  https://github.com/bluescarni/heyoka
  https://bluescarni.github.io/heyoka/
  https://www.reddit.com/r/cpp/comments/lkcjki/solving_differential_equations_with_llvm/

• Some additional information to install CUDA on a Linux device (Ubuntu).
  https://gist.github.com/kmhofmann/cee7c0053da8cc09d62d74a6a4c1c5e4

• importlib: Library and code to import libraries at runtime.
  https://stackoverflow.com/questions/3131217/error-handling-when-importing-modules

• Collection of paper on the topic
  https://github.com/bismex/Awesome-person-re-identification

-- UNet

• UNet: semantic segmentation with PyTorch
  https://github.com/milesial/Pytorch-UNet

-- Deep Learning

• Awesome production machine learning
  https://github.com/EthicalML/awesome-production-machine-learning#data-labelling-tools-and-frameworks

• Background Matting: The World is Your Green Screen
  https://github.com/senguptaumd/Background-Matting

• Real-Time High-Resolution Background Matting
  https://github.com/PeterL1n/BackgroundMattingV2

-- Convolutional

• Convolutional Neural Network Visualizations This repository contains a number of convolutional neural network visualization techniques implemented in PyTorch.
  https://github.com/utkuozbulak/pytorch-cnn-visualizations

• EOS
  eos: A lightweight header-only 3D Morphable Face Model fitting library in modern C++11/14.
  eos is a lightweight 3D Morphable Face Model fitting library that provides basic functionality to use face models
  https://github.com/patrikhuber/eos

-- Pytorch
This repository provides tutorial code for deep learning researchers to learn PyTorch.
https://github.com/Khoronus/pytorch-tutorial

-- Tensorflow

• Face Recognition using Tensorflow This is a TensorFlow implementation of the face recognizer described in the paper "FaceNet: A Unified Embedding for Face Recognition and Clustering"
  https://github.com/KhoronusFork/facenet

-- Queue (C++)

• moodycamel::ConcurrentQueue
  An industrial-strength lock-free queue for C++.
  https://github.com/cameron314/concurrentqueue

-- C++ (C++11)

• Cereal
  cereal - A C++11 library for serialization https://github.com/USCiLab/cereal

https://paperswithcode.com/method/normalizing-flows
https://github.com/janosh/awesome-normalizing-flows

https://github.com/VowpalWabbit/vowpal_wabbit

https://github.com/sxfduter/monocular-depth-estimation https://github.com/rvarun7777/FisheyeDistanceNet https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods https://github.com/ZhixinLai/3D-detection-with-monocular-RGB-image https://github.com/sxfduter/monocular-depth-estimation

https://github.com/svip-lab/PlanarReconstruction https://github.com/NVlabs/planercnn

https://github.com/AliaksandrSiarohin/pose-gan
https://github.com/rohitrango/Adversarial-Pose-Estimation
https://github.com/llcshappy/Monocular-3D-Human-Pose https://github.com/yas-sim/human-pose-estimation-2d-demo https://github.com/chaneyddtt/weakly-supervised-3d-pose-generator https://github.com/vchoutas/expose https://github.com/mks0601/3DMPPE_POSENET_RELEASE https://github.com/isarandi/metrabs

https://esslab.jp/~ess/en/research/pose3d/ https://openaccess.thecvf.com/content_ECCVW_2018/papers/11132/Drover_Can_3D_Pose_be_Learned_from_2D_Projections_Alone_ECCVW_2018_paper.pdf

https://github.com/mhnasseri/sort_oh

https://github.com/princeton-vl/RAFT

https://github.com/Li-Chongyi/Lighting-the-Darkness-in-the-Deep-Learning-Era-Open

Online Learning https://github.com/VowpalWabbit/vowpal_wabbit

Exploration https://github.com/jangirrishabh/Overcoming-exploration-from-demos

Multi agent https://github.com/ai4ce/DiscoNet

Noise https://github.com/lmas/opensimplex

Meta learning https://github.com/learnables/learn2learn

https://github.com/BachiLi/redner
https://mitsuba2.readthedocs.io/en/latest/src/getting_started/intro.html

https://github.com/alphacep/vosk-api