{"id":22968505,"url":"https://github.com/awesomelistsio/awesome-computer-vision","last_synced_at":"2025-10-22T15:31:36.237Z","repository":{"id":263339541,"uuid":"890072603","full_name":"awesomelistsio/awesome-computer-vision","owner":"awesomelistsio","description":"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 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Awesome Lists","Other Lists"],"sub_categories":["TeX Lists","Research Papers"],"readme":"# Awesome Computer Vision [![Awesome Lists](https://srv-cdn.himpfen.io/badges/awesome-lists/awesomelists-flat.svg)](https://github.com/awesomelistsio/awesome)\n\n[![Ko-Fi](https://srv-cdn.himpfen.io/badges/kofi/kofi-flat.svg)](https://ko-fi.com/awesomelists) \u0026nbsp; [![PayPal](https://srv-cdn.himpfen.io/badges/paypal/paypal-flat.svg)](https://www.paypal.com/donate/?hosted_button_id=3LLKRXJU44EJJ) \u0026nbsp; [![Stripe](https://srv-cdn.himpfen.io/badges/stripe/stripe-flat.svg)](https://tinyurl.com/e8ymxdw3) \u0026nbsp; [![X](https://srv-cdn.himpfen.io/badges/twitter/twitter-flat.svg)](https://x.com/ListsAwesome) \u0026nbsp; [![Facebook](https://srv-cdn.himpfen.io/badges/facebook-pages/facebook-pages-flat.svg)](https://www.facebook.com/awesomelists)\n\n\u003e 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.\n\n## Contents\n\n- [Libraries and Frameworks](#libraries-and-frameworks)\n- [Tools and Applications](#tools-and-applications)\n- [Object Detection](#object-detection)\n- [Image Segmentation](#image-segmentation)\n- [3D Computer Vision](#3d-computer-vision)\n- [Face Recognition](#face-recognition)\n- [Datasets](#datasets)\n- [Research Papers](#research-papers)\n- [Learning Resources](#learning-resources)\n- [Books](#books)\n- [Community](#community)\n- [Contribute](#contribute)\n- [License](#license)\n\n## Libraries and Frameworks\n\n- [OpenCV](https://opencv.org/) - An open-source library providing computer vision and machine learning algorithms for image and video analysis.\n- [Detectron2](https://github.com/facebookresearch/detectron2) - A high-performance object detection library developed by Facebook AI Research.\n- [Dlib](http://dlib.net/) - A modern C++ toolkit with machine learning algorithms and tools for computer vision.\n- [Scikit-Image](https://scikit-image.org/) - A Python library for image processing, built on top of SciPy.\n- [Mediapipe](https://mediapipe.dev/) - A cross-platform framework by Google for building multimodal ML solutions, including face detection and pose estimation.\n- [DeepLabV3](https://github.com/tensorflow/models/tree/master/research/deeplab) - A deep learning model for semantic image segmentation.\n- [YOLO (You Only Look Once)](https://github.com/AlexeyAB/darknet) - A real-time object detection system.\n- [MMDetection](https://github.com/open-mmlab/mmdetection) - An open-source object detection toolbox based on PyTorch.\n\n## Tools and Applications\n\n- [LabelImg](https://github.com/tzutalin/labelImg) - An open-source image annotation tool for labeling datasets.\n- [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) - A real-time multi-person detection library for human pose estimation.\n- [VGG Image Annotator (VIA)](https://www.robots.ox.ac.uk/~vgg/software/via/) - A lightweight tool for manual image annotation.\n- [DeepFaceLab](https://github.com/iperov/DeepFaceLab) - A tool for creating deepfakes using face swapping.\n- [ImageAI](https://github.com/OlafenwaMoses/ImageAI) - A Python library built to empower developers to build applications using deep learning for computer vision.\n\n## Object Detection\n\n- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015)](https://arxiv.org/abs/1506.01497) - The paper introducing Faster R-CNN for object detection.\n- [SSD: Single Shot MultiBox Detector (2016)](https://arxiv.org/abs/1512.02325) - A method for real-time object detection.\n- [YOLO: You Only Look Once - Unified, Real-Time Object Detection (2016)](https://arxiv.org/abs/1506.02640) - A fast and accurate object detection algorithm.\n- [EfficientDet: Scalable and Efficient Object Detection (2020)](https://arxiv.org/abs/1911.09070) - A model architecture focused on balancing accuracy and efficiency.\n\n## Image Segmentation\n\n- [U-Net: Convolutional Networks for Biomedical Image Segmentation (2015)](https://arxiv.org/abs/1505.04597) - A convolutional neural network designed for biomedical image segmentation.\n- [DeepLabV3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (2018)](https://arxiv.org/abs/1802.02611) - An advanced model for semantic segmentation.\n- [Mask R-CNN (2017)](https://arxiv.org/abs/1703.06870) - An extension of Faster R-CNN for instance segmentation.\n- [PSPNet: Pyramid Scene Parsing Network (2017)](https://arxiv.org/abs/1612.01105) - A semantic segmentation model using a pyramid pooling module.\n\n## 3D Computer Vision\n\n- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017)](https://arxiv.org/abs/1612.00593) - A deep learning model for 3D point cloud processing.\n- [NeRF: Neural Radiance Fields for View Synthesis (2020)](https://arxiv.org/abs/2003.08934) - A model for representing 3D scenes using neural networks.\n- [Open3D](http://www.open3d.org/) - An open-source library for 3D data processing and visualization.\n- [Colmap](https://colmap.github.io/) - A general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline.\n\n## Face Recognition\n\n- [FaceNet: A Unified Embedding for Face Recognition and Clustering (2015)](https://arxiv.org/abs/1503.03832) - A model for face recognition using deep learning.\n- [DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014)](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf) - A method for facial recognition developed by Facebook.\n- [OpenFace](https://cmusatyalab.github.io/openface/) - An open-source deep learning model for face recognition.\n- [DeepFaceLab](https://github.com/iperov/DeepFaceLab) - The leading software for creating deepfakes.\n\n## Datasets\n\n- [ImageNet](https://www.image-net.org/) - A large-scale image dataset used for image classification and object detection.\n- [COCO (Common Objects in Context)](https://cocodataset.org/) - A dataset for object detection, segmentation, and captioning tasks.\n- [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - A dataset for visual object category recognition and detection.\n- [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) - A large-scale face attributes dataset.\n- [KITTI](http://www.cvlibs.net/datasets/kitti/) - A dataset for autonomous driving research.\n\n## Research Papers\n\n- [Visual Attention Mechanisms (2015)](https://arxiv.org/abs/1409.0473) - Introduction of visual attention mechanisms in neural networks.\n- [Self-Supervised Learning for Visual Representation (2020)](https://arxiv.org/abs/2006.10029) - A study on self-supervised learning techniques for computer vision tasks.\n- [BigGAN: Generative Adversarial Networks for Large-Scale Image Synthesis (2018)](https://arxiv.org/abs/1809.11096) - A generative model for high-quality image synthesis.\n\n## Learning Resources\n\n- [Stanford CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) - A popular course on computer vision and convolutional networks.\n- [Deep Learning for Computer Vision](https://www.coursera.org/specializations/deep-learning) - Part of Andrew Ng’s deep learning specialization on Coursera.\n- [PyImageSearch](https://www.pyimagesearch.com/) - A blog and resource hub for computer vision tutorials.\n- [Kaggle: Computer Vision Datasets](https://www.kaggle.com/datasets?tags=13212-computer-vision) - A collection of computer vision datasets on Kaggle.\n\n## Books\n\n- *Deep Learning for Computer Vision* by Rajalingappaa Shanmugamani - A guide to deep learning techniques in computer vision.\n- *Computer Vision: Algorithms and Applications* by Richard Szeliski - A comprehensive book on computer vision algorithms.\n- *Learning OpenCV* by Gary Bradski and Adrian Kaehler - A practical guide to using the OpenCV library.\n\n## Community\n\n- [Reddit: r/ComputerVision](https://www.reddit.com/r/computervision/) - A subreddit for computer vision discussions.\n- [PyImageSearch Community](https://www.pyimagesearch.com/community/) - A forum for discussing computer vision and image processing.\n- [CVPR Conference](https://cvpr2024.thecvf.com/) - The IEEE Conference on Computer Vision and Pattern Recognition.\n- [Kaggle Computer Vision Forum](https://www.kaggle.com/tags/computer-vision/discussion) - A community for computer vision discussions on Kaggle.\n\n## Contribute\n\nContributions are welcome!\n\n## License\n\n[![CC0](https://mirrors.creativecommons.org/presskit/buttons/88x31/svg/by-sa.svg)](http://creativecommons.org/licenses/by-sa/4.0/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fawesomelistsio%2Fawesome-computer-vision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fawesomelistsio%2Fawesome-computer-vision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fawesomelistsio%2Fawesome-computer-vision/lists"}