22 रिपॉजिटरी
Awesome GitHub RepositoriesKeypoint Detection
Algorithms for identifying specific anatomical landmarks in images or video.
Distinguishing note: Focuses on real-time multi-person landmark tracking, distinct from general object detection.
Explore 22 awesome GitHub repositories matching artificial intelligence & ml · Keypoint Detection. Refine with filters or upvote what's useful.
Awesome Keypoint Detection GitHub Repositories
AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
- facebookresearch/detectron2
facebookresearch/detectron2
34,548GitHub पर देखेंDetectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration. The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati
Provides capabilities to locate specific anatomical landmarks on the human body via keypoint detection.
Python - cmu-perceptual-computing-lab/openpose
CMU-Perceptual-Computing-Lab/openpose
34,145GitHub पर देखेंOpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot landmarks. It functions as a multi-person motion tracker, identifying the spatial coordinates of multiple individuals simultaneously within video streams or static images. Beyond two-dimensional detection, the software acts as a three-dimensional kinematics processor, reconstructing spatial movement data from single or multiple synchronized camera perspectives. The system distinguishes itself through a bottom-up approach that utilizes part-affinity fields to associate body parts across
Identifies two-dimensional coordinates for human body, face, hand, and foot features in real-time.
C++caffecomputer-visioncpp - wzmiaomiao/deep-learning-for-image-processing
WZMIAOMIAO/deep-learning-for-image-processing
26,281GitHub पर देखेंThis project is a PyTorch-based computer vision library and deep learning image processing framework. It provides a collection of neural network architectures designed for visual analysis tasks, specifically focusing on image classification, object detection, and semantic segmentation. The toolset implements diverse methodologies for visual recognition, including anchor-free object detection, regional proposal networks, and heatmap-based keypoint estimation. It utilizes both convolutional neural networks for spatial feature extraction and transformer-based self-attention mechanisms to compute
Implements keypoint detection algorithms to locate anatomical or geometric points of interest.
Pythonbilibiliclassificationdeep-learning - vercel/vercel
vercel/vercel
15,738GitHub पर देखेंVercel is a cloud platform for building, deploying, and scaling web applications. It provides a unified infrastructure that automates the build process by detecting project frameworks and distributing static and dynamic content through a global content delivery network. The platform executes application logic using serverless functions that scale automatically based on real-time traffic demand. The platform distinguishes itself through a centralized AI gateway that proxies requests to multiple model providers, enabling standardized authentication, observability, and cost tracking. It supports
Applies movement patterns from a reference video onto a character or subject from a static image.
TypeScriptclicloudcommand - albumentations-team/albumentations
albumentations-team/albumentations
15,308GitHub पर देखेंAlbumentations is a computer vision image augmentation library designed to increase training data diversity for deep learning models. It provides a toolset for applying geometric and color transformations to images and annotations, including a specialized collection of 3D operations for volumetric data used in medical and scientific imaging. The library functions as an image mask and bounding box transformer, automatically updating masks, bounding boxes, and keypoints when images undergo geometric changes. This ensures that spatial alterations remain synchronized across images and their assoc
Recalculates landmark keypoint positions during geometric rotations and flips of training images.
Python - aliaksandrsiarohin/first-order-model
AliaksandrSiarohin/first-order-model
15,003GitHub पर देखेंThis project is a generative adversarial network designed for image animation and motion transfer. It functions as a computer vision framework that synthesizes video sequences by applying motion patterns extracted from a driving video onto a static source image. The model distinguishes itself by using a keypoint-based representation to decouple object appearance from temporal movement. By tracking structural deformations through learned latent coordinates, it performs motion retargeting and synthetic media production without requiring manual annotations or object-specific training data. The
Decouples object appearance from movement by tracking structural deformations through learned latent keypoints.
Jupyter Notebookdeep-learninggenerative-modelimage-animation - paddlepaddle/paddledetection
PaddlePaddle/PaddleDetection
14,243GitHub पर देखेंPaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti
Identifies and tracks specific body keypoints with high accuracy and consistent speed.
Pythonblazefacedeepsortdetr - microsoft/computervision-recipes
microsoft/computervision-recipes
9,866GitHub पर देखेंThis project is a collection of educational resources and implementation frameworks providing deep learning model recipes, code samples, and step-by-step guides for computer vision tasks. It organizes complex workflows into modular recipes and implementation guides to facilitate the building of image and video analysis models. The framework focuses on specialized vision capabilities, including an image similarity framework for fast retrieval and re-ranking, human pose estimation, and video action recognition. It also provides specific tools for crowd density estimation and document image clea
Identifies points of interest on objects using models that detect both the object and its keypoints.
Jupyter Notebookartificial-intelligenceazurecomputer-vision - facebookresearch/maskrcnn-benchmark
facebookresearch/maskrcnn-benchmark
9,370GitHub पर देखेंThis project is a modular PyTorch framework for training and evaluating object detection and instance segmentation models. It serves as a computer vision research tool and a deep learning inference engine designed to identify object locations, classes, and pixel-level masks within images. The framework implements a two-stage inference pipeline that utilizes region proposal networks and a symmetric mask-head architecture. It provides specialized capabilities for instance segmentation, object bounding box detection, and human pose estimation via anatomical keypoint detection. The system includ
Provides keypoint detection to locate anatomical landmarks for human pose estimation.
Python - mvig-sjtu/alphapose
MVIG-SJTU/AlphaPose
8,583GitHub पर देखेंAlphaPose एक डीप लर्निंग पोज़ एस्टिमेशन फ्रेमवर्क और PyTorch कंप्यूटर विज़न लाइब्रेरी है, जिसे इमेज और वीडियो में मानव शरीर, चेहरे, हाथ और पैर के कीपॉइंट्स को डिटेक्ट और ट्रैक करने के लिए डिज़ाइन किया गया है। यह स्केलेटल पोस्चर एस्टिमेशन और मल्टी-पर्सन पोज़ ट्रैकिंग के लिए एक सिस्टम प्रदान करता है। यह प्रोजेक्ट थ्री-डायमेंशनल ह्यूमन पोज़ रिकंस्ट्रक्शन के लिए टूल्स लागू करता है, जो टू-डायमेंशनल इमेज डेटा से जॉइंट पोजीशन्स और बॉडी मेश शेप्स जनरेट करता है। इसमें एक मल्टी-पर्सन पोज़ ट्रैकर भी शामिल है जो लगातार वीडियो फ्रेम्स में कई लोगों की पहचान बनाए रखने में सक्षम है। यह फ्रेमवर्क कंप्यूटर विज़न क्षमताओं की एक विस्तृत श्रृंखला को कवर करता है, जिसमें मल्टी-पर्सन कीपॉइंट लोकलाइज़ेशन, ह्यूमन मोशन ट्रैकिंग और थ्री-डायमेंशनल बॉडी मेश का रिकंस्ट्रक्शन शामिल है।
Locates specific anatomical points on the human body, face, hands, and feet for computer vision analysis.
Python - open-mmlab/mmpose
open-mmlab/mmpose
7,374GitHub पर देखेंMMPose is a PyTorch-based pose estimation toolbox and deep learning training pipeline designed for detecting 2D and 3D keypoints on humans, animals, and faces. It serves as a computer vision model zoo and a framework for both 2D pose estimation and 3D pose lifting. The project is distinguished by its modular architecture and extensibility, employing a registry-based system and hierarchical configurations to allow for custom algorithm integration and model pipeline customization. It supports diverse estimation paradigms, including top-down, bottom-up, and two-stage pose lifting workflows. The
Transforms raw image coordinates into normalized values or Gaussian heatmaps required for model training.
Pythonanimal-pose-estimationbenchmarkcpm - cocodataset/cocoapi
cocodataset/cocoapi
6,377GitHub पर देखेंThis project is a toolkit and API designed for parsing, manipulating, and visualizing image annotations for computer vision tasks. It provides a programming interface to load and organize Common Objects in Context annotations, specifically for object detection, image segmentation, and keypoint estimation. The library includes tools for converting formatted JSON files into data structures that support the analysis of pixel-level masks and skeletal markers. It enables the visual verification of ground truth accuracy by rendering bounding boxes, segmentation masks, and keypoint markers directly
Manages and displays skeletal markers to analyze human pose estimation accuracy.
Jupyter Notebook - shimat/opencvsharp
shimat/opencvsharp
6,011GitHub पर देखेंOpenCVSharp is a .NET library that wraps native OpenCV functions, providing C# developers with access to OpenCV's computer vision capabilities through an API that mirrors the native C/C++ style. It serves as a managed wrapper for image processing, feature detection, object detection, and image manipulation tasks, while also handling automatic disposal of unmanaged OpenCV resources like Mat objects to prevent memory leaks in .NET applications. The library enables keypoint detection and descriptor extraction using algorithms such as AKAZE, BRISK, or FAST, with brute-force or FLANN-based matchin
Detects salient image keypoints and computes their descriptors using AKAZE, BRISK, or FAST.
C#computer-visiondotnetdotnetstandard - roboflow/rf-detr
roboflow/rf-detr
5,643GitHub पर देखेंRF-DETR is a Python library for training and deploying object detection, instance segmentation, and keypoint detection models built on a vision transformer architecture. It provides a unified command-line interface and Python API for the full workflow, from fine-tuning pretrained checkpoints on custom datasets to running inference on images, video files, and live camera streams. The project supports training on datasets in COCO or YOLO format, with automatic format detection and configurable augmentation pipelines. Models can be exported to ONNX, TFLite, or TensorRT for deployment across edge
Ships a vision transformer model for detecting human keypoints in images.
Pythoncomputer-visiondetrinstance-segmentation - facebookresearch/sapiens
facebookresearch/sapiens
5,388GitHub पर देखेंSapiens is a high-resolution human vision model designed for high-precision, human-centric computer vision tasks. It functions as a suite of tools for estimating human pose, depth, and surface geometry. The project utilizes a vision transformer backbone to perform multiple tasks through a shared encoder. This architecture enables the simultaneous prediction of skeletal structures, joint locations, and the distance between a camera and a human subject. The model's capabilities cover human body part segmentation to isolate anatomical regions from backgrounds and surface normal prediction to re
Implements anatomical landmark identification for human joints using heatmap-based probability distributions.
Python - zhec/realtime_multi-person_pose_estimation
ZheC/Realtime_Multi-Person_Pose_Estimation
5,123GitHub पर देखेंयह रीयल-टाइम मानव कीपॉइंट डिटेक्शन के लिए डिज़ाइन किया गया एक मल्टी-पर्सन पोज़ एस्टिमेशन फ्रेमवर्क है। यह एक बॉटम-अप मानव पोज़ एस्टिमेटर के रूप में कार्य करता है जो एक अलग व्यक्ति डिटेक्टर की आवश्यकता के बिना एक दृश्य में सभी लोगों के कंकाल जोड़ों (skeletal joints) की पहचान करता है। यह सिस्टम पोस्चर विश्लेषण के लिए हीटमैप और वेक्टर फ़ील्ड उत्पन्न करने के लिए एक कन्वेन्शनल न्यूरल नेटवर्क मॉडल का उपयोग करता है। यह विशेष रूप से अंगों के स्थान और अभिविन्यास को एन्कोड करने के लिए पार्ट एफ़िनिटी फ़ील्ड्स को लागू करता है, जिससे मॉडल व्यक्तिगत जोड़ों को पूर्ण कंकालों में जोड़ने में सक्षम होता है। यह प्रोजेक्ट कंप्यूटर विज़न मोशन विश्लेषण और डीप लर्निंग पोस्चर विश्लेषण को कवर करता है, जो इमेज और वीडियो डेटा से मानव शरीर के अंगों के स्थानिक निर्देशांक निकालता है।
Simultaneously identifies and marks anatomical keypoints for multiple individuals in a scene.
Jupyter Notebookcaffecomputer-visioncpp11 - roboflow/sports
roboflow/sports
4,881GitHub पर देखेंRoboflow Sports is a sports video analysis system that combines object detection and tracking with bird's-eye field visualization. Its core pipeline detects and tracks players, referees, and balls across video frames, then maps those tracked positions onto a radar-style overhead view of the playing field. The system goes beyond basic detection by localizing field boundaries and key landmarks such as pitch lines and corners, enabling spatial mapping of player positions relative to the field geometry. It classifies detected players by team affiliation through visual feature extraction and clust
Locates predefined landmarks such as pitch lines or court corners to map field geometry and player positions.
Pythoncomputer-visiondeep-learningdeep-neural-networks - cvg/lightglue
cvg/LightGlue
4,625GitHub पर देखेंLightGlue एक डीप लर्निंग फ्रेमवर्क है जिसे इमेजेस के जोड़ों के बीच लोकल फीचर मैचिंग और हाई-स्पीड कॉरेस्पोंडेंस एस्टिमेशन के लिए डिज़ाइन किया गया है। यह एक कंप्यूटर विज़न मैचिंग मॉडल के रूप में कार्य करता है जो अलग-अलग दृष्टिकोणों (viewpoints) में संबंधित की-पॉइंट्स की पहचान करता है। यह सिस्टम एक एडेप्टिव न्यूरल नेटवर्क आर्किटेक्चर का उपयोग करता है जो इनपुट इमेज पेयर्स के आधार पर अपनी गहराई और चौड़ाई को प्रून (prune) करके इन्फरेंस स्पीड को गतिशील रूप से ऑप्टिमाइज़ करता है। यह दृष्टिकोण फीचर डिस्क्रिप्टर्स के बीच सहसंबंधों (correlations) की गणना करने के लिए ट्रांसफॉर्मर-शैली के अटेंशन मैकेनिज्म और क्रॉस-इमेज अटेंशन का उपयोग करता है। मैचिंग प्रक्रिया में एक इटरेटिव रिफाइनमेंट लूप और डायनामिक अर्ली स्टॉपिंग शामिल है ताकि कॉन्फिडेंस थ्रेशोल्ड पूरा होने पर गणना को रोका जा सके। ये क्षमताएं रीयल-टाइम इमेज अलाइनमेंट और न्यूरल नेटवर्क इन्फरेंस ऑप्टिमाइज़ेशन के लिए एक व्यापक कंप्यूटर विज़न पाइपलाइन का समर्थन करती हैं।
Represents image content as a sparse set of local descriptors extracted from keypoints.
Python - leoxiaobin/deep-high-resolution-net.pytorch
leoxiaobin/deep-high-resolution-net.pytorch
4,479GitHub पर देखेंयह प्रोजेक्ट हाई-रिज़ॉल्यूशन रिप्रेजेंटेशन लर्निंग के लिए डिज़ाइन किए गए एक रिसर्च आर्किटेक्चर का PyTorch इम्प्लीमेंटेशन है। यह कंप्यूटर विज़न फ्रेमवर्क सटीक कीपॉइंट डिटेक्शन, ह्यूमन पोज़ एस्टिमेशन और सिमेंटिक इमेज सेगमेंटेशन पर केंद्रित है। यह इम्प्लीमेंटेशन मानव शरीर पर एनाटॉमिकल लैंडमार्क की पहचान करने और ओरिएंटेशन व अलाइनमेंट का विश्लेषण करने के लिए फेशियल कीपॉइंट कोऑर्डिनेट्स का अनुमान लगाने के लिए विशेष टूल्स प्रदान करता है। यह नेटवर्क में हाई-रिज़ॉल्यूशन रिप्रेजेंटेशन बनाए रखने के लिए मल्टी-रिज़ॉल्यूशन पैरेलल स्ट्रीम्स और रिपीटेड मल्टी-स्केल फ्यूजन सिस्टम का उपयोग करता है। यह फ्रेमवर्क ऑब्जेक्ट डिटेक्शन, इमेज क्लासिफिकेशन और पिक्सेल-लेवल सिमेंटिक सेगमेंटेशन सहित कंप्यूटर विज़न कार्यों की एक विस्तृत श्रृंखला को कवर करता है। इसमें लेबल किए गए डेटासेट पर मॉडल को ट्रेन करने और वैलिडेशन डेटा के मुकाबले अनुमानित जॉइंट पोजीशन्स की सटीकता का मात्रात्मक मूल्यांकन करने के लिए वर्कफ़्लो भी शामिल है।
Provides a toolkit for training and evaluating models that locate anatomical landmarks on the human body.
Cuda - bnsreenu/python_for_microscopists
bnsreenu/python_for_microscopists
4,402GitHub पर देखेंThis project is a Python bio-imaging toolkit and analysis suite designed for processing and analyzing microscopy and medical images. It provides a collection of tools for image quantification, medical image segmentation, and general bio-imaging workflows. The suite includes specialized capabilities for quantifying biological data, such as measuring neuron branching complexity via Sholl analysis, calculating particle size distributions, and tracking wound area in scratch assays. It also features a medical image segmentation library that implements U-Net architectures for isolating anatomical s
Identifies unique regions like corners that remain consistent across rotations, scaling, and distortions.
Jupyter Notebook
