| Book | Links |
|---|---|
| Nixon, Mark, and Alberto Aguado. “Feature extraction and image processing for computer vision.” Academic press, (2019). | goodreads |
| González, Rafael Corsino and Richard E. Woods. “Digital image processing, 4th Edition.” (2018). | goodreads |
| Prince, Simon. “Computer Vision: Models, Learning, and Inference.” (2012). | goodreads |
| Forsyth, David Alexander and Jean Ponce. “Computer Vision - A Modern Approach, Second Edition.” (2011). | goodreads |
| Szeliski, Richard. “Computer Vision - Algorithms and Applications.” Texts in Computer Science (2010). | goodreads |
| Bishop, Charles M.. “Pattern recognition and machine learning, 5th Edition.” Information science and statistics (2007). | goodreads |
| Harltey, Andrew and Andrew Zisserman. “Multiple view geometry in computer vision (2. ed.).” (2003). | goodreads |
| Stockman, George C. and Linda G. Shapiro. “Computer Vision.” (2001). | goodreads |
| Course | Tags | Platform |
|---|---|---|
| Version Control with Git | Git |
Udacity |
| Git Essential Training | Git |
LinkedIn Learning |
| Learning GitHub | Git |
LinkedIn Learning |
| Introduction to Python Programming | Programming |
Udacity |
| Learning Python | Programming |
LinkedIn Learning |
| Intro to Data Science | Data Science |
Udacity |
| Intro to Data Analysis | Data Science |
Udacity |
| Python Data Analysis | Data Science |
LinkedIn Learning |
| Segmentation and Clustering | Data Science |
Udacity |
| Python for Data Science Essential Training Part 1 | Data Science |
LinkedIn Learning |
| Python for Data Science Essential Training Part 2 | Data Science |
LinkedIn Learning |
| Introduction to Machine Learning Course | Machine Learning |
Udacity |
| Machine Learning with Scikit-Learn | Machine Learning |
LinkedIn Learning |
| Intro to Deep Learning with PyTorch | Deep Learning |
Udacity |
| Introduction to Computer Vision | Computer Vision |
Udacity |
| OpenCV for Python Developers | Computer Vision |
LinkedIn Learning |
| Course | Tags | University |
|---|---|---|
| Introduction to Computer Vision | Computer Vision |
Brown |
| Advances in Computer Vision | Computer Vision |
MIT |
| Deep Learning for Computer Vision | Computer Vision Deep Learning |
Stanford |
| Course | Year | Instructor | University |
|---|---|---|---|
| Computer Vision | 2021 | Andreas Geiger | University of Tübingen |
| Computer Vision | 2021 | Yogesh S Rawat / Mubarak Shah | University of Central Florida |
| Advanced Computer Vision | 2021 | Mubarak Shah | University of Central Florida |
| Deep Learning for Computer Vision | 2020 | Justin Johnson | University of Michigan |
| Advanced Deep Learning for Computer Vision | 2020 | Laura Leal-Taixé / Matthias Niessner | Technical University of Munich |
| Introduction to Digital Image Processing | 2020 | Ahmadreza Baghaie | New York Institute of Technology |
| Quantitative Imaging | 2019 | Kevin Mader | ETH Zurich |
| Convolutional Neural Networks for Visual Recognition | 2017 | Fei-Fei Li | Stanford University |
| Introduction to Digital Image Processing | 2015 | Rich Radke | Rensselaer Polytechnic Institute |
| Machine Learning for Robotics and Computer Vision | 2014 | Rudolph Triebel | Technical University of Munich |
| Multiple View Geometry | 2013 | Daniel Cremers | Technical University of Munich |
| Variational Methods for Computer Vision | 2013 | Daniel Cremers | Technical University of Munich |
| Computer Vision | 2012 | Mubarak Shah | University of Central Florida |
| Image and video processing | - | Guillermo Sapiro | Duke University |
| Library | Description |
|---|---|
| OpenCV | Open Source Computer Vision Library |
| Pillow | The friendly PIL fork (Python Imaging Library) |
| scikit-image | collection of algorithms for image processing |
| SciPy | open-source software for mathematics, science, and engineering |
| mmcv | OpenMMLab foundational library for computer vision research |
| imutils | A series of convenience functions to make basic image processing operations |
| pgmagick | python based wrapper for GraphicsMagick/ImageMagick |
| Mahotas | library of fast computer vision algorithms (last updated: 2021) |
| SimpleCV | The Open Source Framework for Machine Vision (last updated: 2015) |
| Library | Description |
|---|---|
| PMT | Piotr's Computer Vision Matlab Toolbox |
| matlabfns | MATLAB and Octave Functions for Computer Vision and Image Processing, P. Kovesi, University of Western Australia |
| VLFeat | open source library implements popular computer vision algorithms, A. Vedaldi and B. Fulkerson |
| MLV | Mid-level Vision Toolbox (MLVToolbox), BWLab, University of Toronto |
| ElencoCode | Loris Nanni's CV functions, University of Padova |
Tags: Object Classification [ObjCls], Object Detection [ObjDet], Object Segmentation [ObjSeg], General Library [GenLib], Text Reading / Object Character Recognition [OCR], Action Recognition [ActRec], Object Tracking [ObjTrk], Data Augmentation [DatAug], Simultaneous Localization and Mapping [SLAM], Outlier/Anomaly/Novelty Detection [NvlDet], Content-based Image Retrieval [CBIR], Image Enhancement [ImgEnh], Aesthetic Assessment [AesAss], Explainable Artificial Intelligence [XAI], Text-to-Image Generation [TexImg], Pose Estimation [PosEst], Video Matting [VidMat], Eye Tracking [EyeTrk]
| Repo | Tags | Description |
|---|---|---|
| computervision-recipes | [GenLib] |
Microsoft, Best Practices, code samples, and documentation for Computer Vision |
| FastAI | [GenLib] |
FastAI, Library over PyTorch used for learning and practicing machine learning and deep learning |
| pytorch-lightning | [GenLib] |
PyTorchLightning, Lightweight PyTorch wrapper for high-performance AI research |
| ignite | [GenLib] |
PyTorch, High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently |
| pytorch_geometric | [GenLib] |
Graph Neural Network Library for PyTorch |
| kornia | [GenLib] |
Open Source Differentiable Computer Vision Library |
| ncnn | [GenLib] |
Tencent, High-performance neural network inference framework optimized for the mobile platform |
| MediaPipe | [ObjDet] [ObjSeg] [ObjTrk] [GenLib] |
Google, iOS - Andriod - C++ - Python - Coral, Face Detection - Face Mesh - Iris - Hands - Pose - Holistic - Hair Segmentation - Object Detection - Box Tracking - Instant Motion Tracking - Objectron - KNIFT (Similar to SIFT) |
| PyTorch image models | [ObjCls] |
rwightman, PyTorch image classification models, scripts, pretrained weights |
| mmclassification | [ObjCls] |
OpenMMLab, Image Classification Toolbox and Benchmark |
| vit-pytorch | [ObjCls] |
SOTA for vision transformers |
| face_classification | [ObjCls] [ObjDet] |
Real-time face detection and emotion/gender classification |
| mmdetection | [ObjDet] |
OpenMMLab, Image Detection Toolbox and Benchmark |
| detectron2 | [ObjDet] [ObjSeg] |
Facebook, FAIR's next-generation platform for object detection, segmentation and other visual recognition tasks |
| detr | [ObjDet] |
Facebook, End-to-End Object Detection with Transformers |
| libfacedetection | [ObjDet] |
An open source library for face detection in images, speed: ~1000FPS |
| FaceDetection-DSFD | [ObjDet] |
Tencent, SOTA face detector |
| object-Detection-Metrics | [ObjDet] |
Most popular metrics used to evaluate object detection algorithms |
| SAHI | [ObjDet] [ObjSeg] |
A lightweight vision library for performing large scale object detection/ instance segmentation |
| yolov5 | [ObjDet] |
ultralytics |
| AlexeyAB/darknet pjreddie/darknet | [ObjDet] |
YOLOv4 / Scaled-YOLOv4 / YOLOv3 / YOLOv2 |
| U-2-Net | [ObjDet] |
ultralytics U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection |
| segmentation_models.pytorch | [ObjSeg] |
qubvel, PyTorch segmentation models with pretrained backbones |
| mmsegmentation | [ObjSeg] |
OpenMMLab, Semantic Segmentation Toolbox and Benchmark |
| mmocr | [OCR] |
OpenMMLab, Text Detection, Recognition and Understanding Toolbox |
| pytesseract | [OCR] |
A Python wrapper for Google Tesseract |
| EasyOCR | [OCR] |
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc |
| PaddleOCR | [OCR] |
Practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices |
| mmtracking | [ObjTrk] |
OpenMMLab, Video Perception Toolbox for object detection and tracking |
| mmaction | [ActRec] |
OpenMMLab, An open-source toolbox for action understanding based on PyTorch |
| albumentations | [DatAug] |
Fast image augmentation library and an easy-to-use wrapper around other libraries |
| ORB_SLAM2 | [SLAM] |
Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities |
| pyod | [NvlDet] |
Python Toolbox for Scalable Outlier Detection (Anomaly Detection) |
| imagededup | [CBIR] |
Image retrieval, CBIR, Find duplicate images made easy! |
| image-match | [CBIR] |
Image retrieval, CBIR, Quickly search over billions of images |
| Bringing-Old-Photos-Back-to-Life | [ImgEnh] |
Microsoft, Bringing Old Photo Back to Life (CVPR 2020 oral) |
| image-quality-assessment | [AesAss] |
Idealo, Image Aesthetic, NIMA model to predict the aesthetic and technical quality of images |
| aesthetics | [AesAss] |
Image Aesthetics Toolkit using Fisher Vectors |
| pytorch-cnn-visualizations | [XAI] |
Pytorch implementation of convolutional neural network visualization techniques |
| DALLE2-pytorch | [TexImg] |
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch |
| imagen-pytorch | [TexImg] |
Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch |
| openpose | [PosEst] |
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation |
| RobustVideoMatting | [VidMat] |
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML! |
| fastudp | [NvlDet] [CBIR] |
An unsupervised and free tool for image and video dataset analysis |
| Random-Erasing | [DatAug] |
Random Erasing Data Augmentation in PyTorch |
| CutMix-PyTorch | [DatAug] |
Official Pytorch implementation of CutMix regularizer |
| keras-cv | [GenLib] |
Library of modular computer vision oriented Keras components |
| PsychoPy | [EyeTrk] |
Library for running psychology and neuroscience experiments |
- PyTorch - CV Datasets, Meta
- Tensorflow - CV Datasets, Google
- CVonline: Image Databases, Edinburgh University, Thanks to Robert Fisher!
- Yet Another Computer Vision Index To Datasets (YACVID), Thanks to Hayko Riemenschneider!
- Kaggle
- PaperWithCode, Meta
- RoboFlow
- VisualData
- CUHK Computer Vision
- VGG - University of Oxford
- MLflow, Platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry
- Apache Airflow, Apache/AirBnB, Platform created by the community to programmatically author, schedule and monitor workflows
- Ploomber, fastest way to build data pipelines.
- VoTT, Microsoft, Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos
- labelme, Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation)
- labelImg, Graphical image annotation tool and label object bounding boxes in images
- VIA, VGG Oxford, HTML-based standalone manual annotation software for image, audio and video
- FiftyOne, open-source tool for building high-quality datasets and computer vision models
- anomaly-detection-resources, Anomaly detection related books, papers, videos, and toolboxes
- awesome-satellite-imagery-datasets List of satellite image training datasets with annotations for computer vision and deep learning
- awesome-Face_Recognition, Computer vision papers about faces.
- the-incredible-pytorch, Curated list of tutorials, papers, projects, communities and more relating to PyTorch
- How to build a good poster - [Link1] [Link2] [Link3]
- How to report a good report - [Link1] [link2]
- The "Python Machine Learning (3rd edition)" book code repository
- Multithreading with OpenCV-Python to improve video processing performance
- Computer Vision Zone - Videos and implementations for computer vision projects
- MadeWithML, Learn how to responsibly deliver value with ML
- d2l-en, Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 200 universities
- Writing Pet Peeves, writing guide for correctness, references, and style
- Hitchhiker's Guide to Python, Python best practices guidebook, written for humans
- python-fire, Google, a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
- shotcut, a free, open source, cross-platform video editor.
- PyTorch Computer Vision Cookbook, PyTorch Computer Vision Cookbook, Published by Packt.
- Machine Learning Mastery - Blogs, Blogs written by Jason Brownlee about machine learning.
- PyImageSearch - Blogs, Blogs written by Adrian Rosebrock about computer vision.
- jetson-inference, guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
- CORE Rank A:
- ICCV: International Conference on Computer Vision (IEEE)
- CVPR: Conference on Computer Vision and Pattern Recognition (IEEE)
- ECCV: European Conference on Computer Vision (Springer)
- WACV: Winter Conference/Workshop on Applications of Computer Vision (IEEE)
- ICASSP: International Conference on Acoustics, Speech, and Signal Processing (IEEE)
- MICCAI: Conference on Medical Image Computing and Computer Assisted Intervention (Springer)
- IROS: International Conference on Intelligent Robots and Systems (IEEE)
- ACMMM: ACM International Conference on Multimedia (ACM)
- CORE Rank B
- ACCV: Asian Conference on Computer Vision (Springer)
- VCIP: International Conference on Visual Communications and Image Processing (IEEE)
- ICIP: International Conference on Image Processing (IEEE)
- CAIP: International Conference on Computer Analysis of Images and Patterns (Springer)
- VISAPP: International Conference on Vision Theory and Applications (SCITEPRESS)
- ICPR: International Conference on Pattern Recognition (IEEE)
- ACIVS: Conference on Advanced Concepts for Intelligent Vision Systems (Springer)
- EUSIPCO: European Signal Processing Conference (IEEE)
- ICRA: International Conference on Robotics and Automation (IEEE)
- BMVC: British Machine Vision Conference (organized by BMVA: British Machine Vision Association and Society for Pattern Recognition)
- CORE Rank C:
- ICISP: International Conference on Image and Signal Processing (Springer)
- ICIAR: International Conference on Image Analysis and Recognition (Springer)
- ICVS: International Conference on Computer Vision Systems (Springer)
- Unranked but popular
- MIUA: Conference on Medical Image Understanding and Analysis (organized by BMVA: British Machine Vision Association and Society for Pattern Recognition)
- EUVIP: European Workshop on Visual Information Processing (IEEE, organized by EURASIP: European Association for Signal Processing)
- CIC: Color and Imaging Conference (organized by IS&T: Society for Imaging Science and Technology)
- CVCS: Colour and Visual Computing Symposium
- DSP: International Conference on Digital Signal Processing
- Tier 1
- IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
- IEEE Transactions on Image Processing (IEEE TIP)
- IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT)
- Springer International Journal of Computer Vision (Springer IJCV)
- Elsevier Pattern Recognition (Elsevier PR)
- Elsevier Computer Vision and Image Understanding (Elsevier CVIU)
- Elsevier Expert Systems with Applications
- Elsevier Neurocomputing, Springer Neural Computing and Applications
- Tier 2
- Elsevier Image and Vision Computing (Elsevier IVC)
- Elsevier Pattern Recognition Letters (Elsevier PR Letters)
- Elsevier Journal of Visual Communication and Image Representation
- Springer Journal of Mathematical Imaging and Vision
- SPIE Journal of Electronic Imaging
- IET Image Processing
- Springer Pattern Analysis and Applications (Springer PAA)
- Springer Machine Vision and Applications (Springer MVA)
- IET Computer Vision
- Open Access
- IEEE Access
- MDPI Journal of Imaging
- International Computer Vision Summer School (IVCSS) [2007-Present], Sicily, Italy [2023]
- Machine Intelligence and Visual Computing Summer School (VISUM) [2013-2022], Porto, Portugal [2022]
- BMVA British Computer Vision Summer School (CVSS) [2013-2020,2023], UK [Website]
-
@AurelienGeron
[Individual], Aurélien Géron: former lead of YouTube's video classification team, and author of the O'Reilly book Hands-On Machine Learning with Scikit-Learn and TensorFlow. -
@howardjeremyp
[Individual], Jeremy Howard: former president and chief scientist of Kaggle, and co-founder of fast.ai. -
@PieterAbbeel
[Individual], Pieter Abbeel: professor of electrical engineering and computer sciences, University of California, Berkeley. -
@pascalpoupart3507
[Individual], Pascal Poupart: professor in the David R. Cheriton School of Computer Science at the University of Waterloo. -
@MatthiasNiessner
[Individual], Matthias Niessner: Professor at the Technical University of Munich and head of the Visual Computing Lab. -
@MichaelBronsteinGDL
[Individual], Michael Bronstein: DeepMind Professor of AI, University of Oxford / Head of Graph Learning Research, Twitter. -
@DeepFindr
[Individual], Videos about all kinds of Machine Learning / Data Science topics. -
@deeplizard
[Individual], Videos about building collective intelligence. -
@YannicKilcher
[Individual], Yannic Kilcher: make videos about machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society. -
@sentdex
[Individual], sentdex: provides Python programming tutorials in machine learning, finance, data analysis, robotics, web development, game development and more. -
@bmvabritishmachinevisionas8529
[Conferences], BMVA: British Machine Vision Association. -
@ComputerVisionFoundation
[Conferences], Computer Vision Foundation (CVF): co-sponsored conferences on computer vision (e.g. CVPR and ICCV). -
@cvprtum
[University], Computer Vision Group at Technical University of Munich. -
@UCFCRCV
[University], Center for Research in Computer Vision at University of Central Florida. -
@dynamicvisionandlearninggr1022
[University], Dynamic Vision and Learning research group channel! Technical University of Munich. -
@TubingenML
[University], Machine Learning groups at the University of Tübingen. -
@computervisiontalks4659
[Talks], Computer Vision Talks. -
@freecodecamp
[Talks], Videos to learn how to code. -
@LondonMachineLearningMeetup
[Talks], Largest machine learning community in Europe. -
@LesHouches-iu6nv
[Talks], Summer school on Statistical Physics of Machine learning held in Les Houches, July 4 - 29, 2022. -
@MachineLearningStreetTalk
[Talks], top AI podcast on Spotify. -
@WeightsBiases
[Talks], Weights and Biases team's conversations with industry experts, and researchers. -
@PreserveKnowledge
[Talks], Canada higher education media organization that focuses on advances in mathematics, computer science, and artificial intelligence. -
@TwoMinutePapers
[Papers], Two Minute Papers: Explaining AI papers in few mins. -
@TheAIEpiphany
[Papers], Aleksa Gordić: x-Google DeepMind, x-Microsoft engineer explaining AI papers. -
@bycloudAI
[Papers], bycloud: covers the latest AI tech/research papers for fun. -
Unorganized/Unsorted:
- https://www.youtube.com/@AAmini
- https://www.youtube.com/@WhatsAI
- https://www.youtube.com/@mrdbourke
- https://www.youtube.com/@marksaroufim
- https://www.youtube.com/@NicholasRenotte
- https://www.youtube.com/@abhishekkrthakur
- https://www.youtube.com/@AladdinPersson
- https://www.youtube.com/@CodeEmporium
- https://www.youtube.com/@arp_ai
- https://www.youtube.com/@CodeThisCodeThat
- https://www.youtube.com/@connorshorten6311
- https://www.youtube.com/@SmithaKolan
- https://www.youtube.com/@AICoffeeBreak
- https://www.youtube.com/@independentcode
- https://www.youtube.com/@alfcnz
- https://www.youtube.com/@KapilSachdeva
- https://www.youtube.com/@AICoding
- https://www.youtube.com/@mildlyoverfitted
- Vision Science, announcements about industry/academic jobs in computer vision around the world (in English).
- bull-i3, posts about job opportunities in computer vision in France (in French).