README.md

Awesome Computer Vision

     

A curated list of awesome libraries, frameworks, tools, datasets, and research papers in computer vision, covering topics such as object detection, image segmentation, 3D vision, and more.

Contents

• Libraries and Frameworks
• Tools and Applications
• Object Detection
• Image Segmentation
• 3D Computer Vision
• Face Recognition
• Datasets
• Research Papers
• Learning Resources
• Books
• Community
• Contribute
• License

Libraries and Frameworks

• OpenCV - An open-source library providing computer vision and machine learning algorithms for image and video analysis.
• Detectron2 - A high-performance object detection library developed by Facebook AI Research.
• Dlib - A modern C++ toolkit with machine learning algorithms and tools for computer vision.
• Scikit-Image - A Python library for image processing, built on top of SciPy.
• Mediapipe - A cross-platform framework by Google for building multimodal ML solutions, including face detection and pose estimation.
• DeepLabV3 - A deep learning model for semantic image segmentation.
• YOLO (You Only Look Once) - A real-time object detection system.
• MMDetection - An open-source object detection toolbox based on PyTorch.

Tools and Applications

• LabelImg - An open-source image annotation tool for labeling datasets.
• OpenPose - A real-time multi-person detection library for human pose estimation.
• VGG Image Annotator (VIA) - A lightweight tool for manual image annotation.
• DeepFaceLab - A tool for creating deepfakes using face swapping.
• ImageAI - A Python library built to empower developers to build applications using deep learning for computer vision.

Object Detection

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015) - The paper introducing Faster R-CNN for object detection.
• SSD: Single Shot MultiBox Detector (2016) - A method for real-time object detection.
• YOLO: You Only Look Once - Unified, Real-Time Object Detection (2016) - A fast and accurate object detection algorithm.
• EfficientDet: Scalable and Efficient Object Detection (2020) - A model architecture focused on balancing accuracy and efficiency.

Image Segmentation

• U-Net: Convolutional Networks for Biomedical Image Segmentation (2015) - A convolutional neural network designed for biomedical image segmentation.
• DeepLabV3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (2018) - An advanced model for semantic segmentation.
• Mask R-CNN (2017) - An extension of Faster R-CNN for instance segmentation.
• PSPNet: Pyramid Scene Parsing Network (2017) - A semantic segmentation model using a pyramid pooling module.

3D Computer Vision

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017) - A deep learning model for 3D point cloud processing.
• NeRF: Neural Radiance Fields for View Synthesis (2020) - A model for representing 3D scenes using neural networks.
• Open3D - An open-source library for 3D data processing and visualization.
• Colmap - A general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline.

Face Recognition

• FaceNet: A Unified Embedding for Face Recognition and Clustering (2015) - A model for face recognition using deep learning.
• DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014) - A method for facial recognition developed by Facebook.
• OpenFace - An open-source deep learning model for face recognition.
• DeepFaceLab - The leading software for creating deepfakes.

Datasets

• ImageNet - A large-scale image dataset used for image classification and object detection.
• COCO (Common Objects in Context) - A dataset for object detection, segmentation, and captioning tasks.
• PASCAL VOC - A dataset for visual object category recognition and detection.
• CelebA - A large-scale face attributes dataset.
• KITTI - A dataset for autonomous driving research.

Research Papers

• Visual Attention Mechanisms (2015) - Introduction of visual attention mechanisms in neural networks.
• Self-Supervised Learning for Visual Representation (2020) - A study on self-supervised learning techniques for computer vision tasks.
• BigGAN: Generative Adversarial Networks for Large-Scale Image Synthesis (2018) - A generative model for high-quality image synthesis.

Learning Resources

• Stanford CS231n: Convolutional Neural Networks for Visual Recognition - A popular course on computer vision and convolutional networks.
• Deep Learning for Computer Vision - Part of Andrew Ng’s deep learning specialization on Coursera.
• PyImageSearch - A blog and resource hub for computer vision tutorials.
• Kaggle: Computer Vision Datasets - A collection of computer vision datasets on Kaggle.

Books

• Deep Learning for Computer Vision by Rajalingappaa Shanmugamani - A guide to deep learning techniques in computer vision.
• Computer Vision: Algorithms and Applications by Richard Szeliski - A comprehensive book on computer vision algorithms.
• Learning OpenCV by Gary Bradski and Adrian Kaehler - A practical guide to using the OpenCV library.

Community

• Reddit: r/ComputerVision - A subreddit for computer vision discussions.
• PyImageSearch Community - A forum for discussing computer vision and image processing.
• CVPR Conference - The IEEE Conference on Computer Vision and Pattern Recognition.
• Kaggle Computer Vision Forum - A community for computer vision discussions on Kaggle.

Contribute

Contributions are welcome!

License