awesome-cv
Collection of useful sources that related to computer vision mostly.
https://github.com/mucozcan/awesome-cv
Last synced: about 2 hours ago
JSON representation
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OCR
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Video Streaming
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Loss Functions
- Repo of collected sources and projects related to GStreamer
- Gstreamer command-line cheat sheet
- How to setup an RTSP server on Headless Raspberry Pi and stream RPi Camera data using GStreamer?
- GStreamer Tutorials
- GStreamer UDP stream examples
- Install All Essential Media Codecs in Ubuntu With This Single Command
- What’s the Difference Between Codecs & Containers?
- Gstreamer basic real time streaming tutorial
- Stream Video using Gstreamer RTSP Server
- Accelerated GStreamer User Guide
- Video streaming from Raspberry PI - Python vs. raspivid + netcat
- complete list of ffmpeg flags / commands
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Production Level
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Loss Functions
- TensorFlow Lite Flutter Plugin
- FFmpeg plugin for Flutter
- NVIDIA Jetson Nano and NVIDIA Jetson AGX Xavier for Kubernetes (K8s) and machine learning (ML) for smart IoT
- Submit it!: a lightweight tool for submitting Python functions for computation within a Slurm cluster
- Curated papers, articles, and blogs on data science & machine learning in production
- Jetson-Inference: AI Deployment Library & Guide
- Optimizing TensorFlow Models for Serving (Google Cloud AI Huddle)
- Implementing YOLOv4 with TensorFlow, TFLite and TensorRT
- How to Build Object Detection APIs Using TensorFlow and Flask
- An introduction to MLOps on Google Cloud
- Accelerating Machine Learning App Development with Kubeflow Pipelines (Cloud Next '19)
- Best 8 Machine Learning Model Deployment Tools That You Need to Know
- A friendly introduction to machine learning compilers and optimizers
- How to consume C++ code in Swift
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IoT
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Tools
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Loss Functions
- FiftyOne: The open-source tool for building high-quality datasets and computer vision models
- MLOps-Basics
- Optuna - A hyperparameter optimization framework
- A Neural Network Playground
- Make Sense - Image Annotation Tool
- Netron - Neural Network Viewer
- Lens Focal Length and Stereo Baseline Calculator
- pytest: helps you write better programs
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Mix of interesting and cool stuff
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Digital Image Processing
- Caire: a content aware image resize library
- Principal Component Analysis
- Optical Flow in OpenCV (C++/Python)
- Principal Component Analysis | LearnOpenCV
- Image Processing: What are occlusions?
- Faster alternatives for calcOpticalFlowSF
- Intro to Digital Image Processing (ECSE-4540) Lectures, Spring 2015
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Deep Learning
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General
- Deep Learning Materials by Deep Learning Wizard
- The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
- Awesome-Pytorch-list
- Mind Mapping for Depth Estimation
- Neural Networks and Deep Learning(Book)
- Dive into Deep Learning
- Effect of Batch Size on Neural Net Training
- How to avoid machine learning pitfalls: a guide for academic researchers
- A Brief Overview of Loss Functions in Pytorch
- How to avoid machine learning pitfalls: a guide for academic researchers
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Networks
- Intuitively Understanding Convolutions for Deep Learning
- CNN Explainer: Learn Convolutional Neural Network (CNN) in your browser!
- Convolution arithmetic
- Transposed Convolution Demystified
- Image Deblurring using Generative Adversarial Networks
- PyTorch Implementation of ConvLSTM Cell
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Temporal Convolutional Networks and Forecasting
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Convolutional Neural Networks(Stanford Lecture Notes)
- Intuitively Understanding Convolutions for Deep Learning
- How Convolutional Neural Networks work
- How convolutional neural networks work, in depth
- What is the vanishing gradient problem?
- Deep Residual Learning for Image Recognition (Paper Explained)
- Transposed Convolution Demystified
- A Gentle Introduction to Batch Normalization for Deep Neural Networks
- What is skip architecture in CNN?
- Comprehensive look at 1X1 Convolution in Deep Learning
- A Friendly Introduction to Generative Adversarial Networks
- Recurrent Neural Networks - EXPLAINED!
- MIT 6.S191: Recurrent Neural Networks
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
- An introduction to ConvLSTM
- What is the difference between ConvLSTM and CNN LSTM?
- Video Classification with CNN, RNN, and PyTorch
- The Unreasonable Effectiveness of Recurrent Neural Networks
- An Introduction to Autoencoders: Everything You Need to Know
- Comprehensive Introduction to Autoencoders
- What is the main difference between GAN and autoencoder?
- Building Autoencoders in Keras
- Getting the Intuition of Graph Neural Networks
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Understanding Graph Convolutional Networks for Node Classification
- Comprehensive Introduction to Autoencoders
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Understanding Graph Convolutional Networks for Node Classification
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Intuitively Understanding Convolutions for Deep Learning
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Transposed Convolution Demystified
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
- Intuitively Understanding Convolutions for Deep Learning
- Transposed Convolution Demystified
- Comprehensive Introduction to Autoencoders
- Understanding Graph Convolutional Networks for Node Classification
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Problems
- awesome-object-detection
- Non-maximum Suppression (NMS)
- PyTorch Tutorial to Object Detection.(SSD)
- Object Detection Accuracy (mAP) Cheat Sheet
- Real-time Hand-Detection using Neural Networks (SSD) on Tensorflow
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Understanding Deep Learning Techniques for Image Segmentation
- Fully Convolutional Networks for Semantic Segmentation(Paper)
- Real time human head pose estimation using TensorFlow and OpenCV
- Where are they looking? PyTorch Implementation
- Believe It or Not, We Know What You Are Looking at!(PyTorch Implementation)
- DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Zero to Hero: Guide to Object Detection using Deep Learning: Faster R-CNN,YOLO,SSD
- Non-maximum Suppression (NMS)
- Selective Search for Object Detection | R-CNN
- Object Detection Accuracy (mAP) Cheat Sheet
- Tackling the Small Object Problem in Object Detection
- YOLOv4 - Ten Tactics to Build a Better Model
- The Power of Tiling for Small Object Detection
- An Improved Faster R-CNN for Small Object Detection
- Object Detection with RetinaNet(Keras)
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Understanding Deep Learning Techniques for Image Segmentation
- Comparison of Fully Convolutional Networks (FCN) and U-Net for Road Segmentation from High Resolution Imagerie
- Fully Convolutional Networks for Semantic Segmentation(Paper)
- A survey of loss functions for semantic segmentation
- GazeFollow: Where are they looking?(2015)
- Believe It or Not, We Know What You Are Looking at!(Paper-2018)
- Intro to anomaly detection with OpenCV, Computer Vision, and scikit-learn
- Anomaly detection with Keras, TensorFlow, and Deep Learning
- A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies
- DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- DeepSORT: Deep Learning to Track Custom Objects in a Video
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
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Loss Functions
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Math
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Loss Functions
- Kalman and Bayesian Filters in Python
- But what is a partial differential equation?
- The gradient
- Gradient Descent Derivation
- How Gradient Descent Works?
- Intuitive crutches for higher dimensional thinking
- Eigenvectors and eigenvalues
- Making sense of principal component analysis, eigenvectors & eigenvalues(<3)
- Find a point on a rotated rectangle with known angle
- How a Kalman filter works, in pictures
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Datasets
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Loss Functions
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Stereo Vision
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Loss Functions
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- Object Distance Measurement By Stereo Vision
- Image Rectification
- What Is Camera Calibration?
- CS6320: 3D Computer Vision Project 2 Stereo and 3D Reconstruction from Disparity
- A High-Precision Calibration Method for Stereo Vision System
- Middlebury Stereo Vision Page
- Multiple View Geometry in Computer Vision Second Edition
- How to verify the correctness of calibration of a webcam?
- Is reprojection error enough in stereo calibration?
- Does a smaller reprojection error always means better calibration?
- What to Expect from a Stereo Vision System
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- Effect of Baseline On Stereo Vision Systems
- How field of view changes depth estimation in stereo vision?
- Design parameters for adjusting the visual field of binocular stereo cameras
- Large-Field-of-View Stereo for Automotive Applications
- Easily Create a Depth Map with Smartphone AR
- Choosing Good Stereo Parameters
- Camera Projection
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
- The Depth II: Block Matching(OpenCV)
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3D Laser Scanning and Structured Light
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Photogrammetry and 3D Reconstruction
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Photography and Camera Specs
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Loss Functions
- Angle of view
- What Is Exposure? (A Beginner’s Guide)
- EXPOSURE TRIANGLE: APERTURE, ISO & SHUTTER SPEED
- What are Global Shutter and Rolling shutter? How to choose the one that fits the application?
- What Is Focal Length in Photography?
- Focal Length vs Effective Focal Length
- What is a pinhole camera?
- What is Lens Distortion?
- Rolling Shutter vs Global Shutter: What’s the difference?
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OOP
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Linux
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Loss Functions
- DebuggingWithGdb
- How to fix a broken package, when “apt-get install -f” does not work?
- Linux From Scratch
- How do I make a RAM disk?
- Better Than Top: 7 System Monitoring Tools for Linux to Keep an Eye on Vital System Stats
- Sharing WiFi Connection over Ethernet on Ubuntu 18.04
- How to check if port is in use on Linux or Unix
- Randomly copy certain amount of certain file type from one directory into another
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Algorithms
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Loss Functions
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General Sources(Computer Vision)
Programming Languages
Categories
Deep Learning
257
Stereo Vision
71
Production Level
14
Video Streaming
12
Math
10
Photography and Camera Specs
9
Datasets
8
Tools
8
Linux
8
Digital Image Processing
7
IoT
4
Photogrammetry and 3D Reconstruction
4
Mix of interesting and cool stuff
4
General Sources(Computer Vision)
4
OCR
3
3D Laser Scanning and Structured Light
3
OOP
2
Algorithms
1
Sub Categories
Keywords
computer-vision
10
deep-learning
10
machine-learning
7
pytorch
6
python
5
object-detection
4
data-science
3
neural-network
3
detection
2
image-processing
2
natural-language-processing
2
robotics
2
ssd
2
raspberry-pi
2
visualization
2
convolutional-neural-networks
2
data-quality
2
python3
2
tensorflow
2
production
1
recsys
1
reinforcement-learning
1
data-engineering
1
data-discovery
1
applied-machine-learning
1
applied-data-science
1
slurm
1
clusters
1
virtualbox
1
tensorflow-serving
1
artificial-intelligence
1
active-learning
1
mqtt-client
1
mqtt-broker
1
mqtt
1
asyncio
1
video-analytics
1
tensorrt
1
segmentation
1
nvidia
1
jetson-xavier-nx
1
jetson-xavier
1
jetson-tx2
1
jetson-tx1
1
jetson-nano
1
jetson
1
inference
1
image-recognition
1
embedded
1
digits
1