Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-cv
Collection of useful sources that related to computer vision mostly.
https://github.com/Ertugrul-Kurubal/awesome-cv
Last synced: 5 days ago
JSON representation
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Deep Learning
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Networks
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Convolution arithmetic
- Image Deblurring using Generative Adversarial Networks
- PyTorch Implementation of ConvLSTM Cell
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- 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)
- A Gentle Introduction to Batch Normalization for Deep Neural Networks
- 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
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
- Intuitively Understanding Convolutions for Deep Learning
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Problems
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- 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
- 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)
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- 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)
- GazeFollow: Where are they looking?(2015)
- Believe It or Not, We Know What You Are Looking at!(Paper-2018)
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
- Non-maximum Suppression (NMS)
- Object Detection Accuracy (mAP) Cheat Sheet
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General
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Stereo Vision
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Problems
- 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)
- Calibration of Stereo Cameras for Mobile Robots
- 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)
- Object Distance Measurement By Stereo Vision
- Variants of depth cameras
- Image Rectification
- ActiveStereoNet: The first deep learning solution for active stereo systems
- What Is Camera Calibration?
- Camera Calibration Video Series
- CS6320: 3D Computer Vision Project 2 Stereo and 3D Reconstruction from Disparity
- 3D, Depth Perception, and Stereo(The Ancient Secrets of Computer Vision )
- A High-Precision Calibration Method for Stereo Vision System
- Epipolar geometry
- Stereo Correspondence Algorithms
- Middlebury Stereo Vision Page
- Multiple View Geometry in Computer Vision Second Edition
- A video that includes techniques for improvement accuracy of stereo system precision
- 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)
- Calibration of Stereo Cameras for Mobile Robots
- Disparity map post-filtering
- 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
- Object Distance Measurement By Stereo Vision
- Does a smaller reprojection error always means better calibration?
- 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)
- 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 II: Block Matching(OpenCV)
- The Depth I: Stereo Calibration and Rectification(OpenCV)
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Datasets
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3D Laser Scanning and Structured Light
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Photogrammetry and 3D Reconstruction
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Video Streaming
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Problems
- 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?
- What’s the Difference Between Codecs & Containers?
- GStreamer Tutorials
- GStreamer UDP stream examples
- Install All Essential Media Codecs in Ubuntu With This Single Command
- Gstreamer basic real time streaming tutorial
- Stream Video using Gstreamer RTSP Server
- The All You Need Guide to Video Codecs and Compression
- Accelerated GStreamer User Guide
- How to Build Gstreamer RTSP Server
- How to make a Raspberry Pi an RTSP streamer and how to consume this?
- 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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Problems
- FFmpeg plugin for Flutter
- TensorFlow Lite Flutter Plugin
- 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
- 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)
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IoT
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Tools
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Mix of interesting and cool stuff
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Photography and Camera Specs
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Problems
- What Is Exposure? (A Beginner’s Guide)
- Rolling Shutter vs Global Shutter: What’s the difference?
- Angle of view
- 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?
- What is Lens Distortion?
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General Sources(Computer Vision)
- Computer Vision for Visual Effects (ECSE-6969) Lectures Spring 2014
- The Ancient Secrets of Computer Vision
- First Principles of Computer Vision Youtube Channel
- Autonomous Vision Group, CVPR 2020 Keynote Talks
- Computer Vision for Visual Effects (ECSE-6969) Lectures Spring 2014
- The Ancient Secrets of Computer Vision
- Autonomous Vision Group, CVPR 2020 Keynote Talks
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Digital Image Processing
- Intro to Digital Image Processing (ECSE-4540) Lectures, Spring 2015
- Image Processing: What are occlusions?
- Ordering coordinates clockwise with Python and OpenCV
- OpenCV Object Tracking
- Principal Component Analysis
- Optical Flow in OpenCV (C++/Python)
- Faster alternatives for calcOpticalFlowSF
- Principal Component Analysis | LearnOpenCV
- Intro to Digital Image Processing (ECSE-4540) Lectures, Spring 2015
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OOP
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Math
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Problems
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Linux
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Problems
- Renaming files in a folder to sequential numbers
- How to Connect to a Raspberry Pi Remotely via SSH
- 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
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Categories
Deep Learning
125
Stereo Vision
81
Video Streaming
15
Photography and Camera Specs
10
Digital Image Processing
9
Linux
9
Production Level
9
General Sources(Computer Vision)
7
Tools
6
Mix of interesting and cool stuff
6
Photogrammetry and 3D Reconstruction
6
3D Laser Scanning and Structured Light
5
Datasets
5
OOP
2
IoT
2
Math
1
Sub Categories
Keywords
deep-learning
6
pytorch
6
computer-vision
6
python
5
neural-network
3
machine-learning
3
tensorflow
2
raspberry-pi
2
ssd
2
convolutional-neural-networks
2
object-detection
2
data-science
2
hand-detection
1
detector
1
tutorial
1
single-shot-multibox-detector
1
single-shot-detection
1
pytorch-tutorial
1
object-recognition
1
detection
1
time-series
1
spatio-temporal
1
rnn
1
pytorch-implementation
1
lstm
1
hand-detector
1
facial-landmarks-detection
1
onnxruntime
1
opencv
1
accv2018
1
gaze-follow
1
3d
1
3d-scanner
1
fabscan-pi
1
hardware
1
javascript
1
python-support
1
python27
1
raspberry
1
threejs
1
3d-reconstruction
1
pytorch-model
1
probabilistic-programming
1
papers
1
nlp-library
1
nlp
1
natural-language-processing
1
facebook
1
cv
1
awesome-list
1