Awesome GitHub RepositoriesKeypoint Detection

Detectron2 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

OpenPose 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

This 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

Vercel 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

Albumentations 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

This 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

PaddleDetection 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

This 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

This 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

AlphaPose es un framework de estimación de pose basado en deep learning y una biblioteca de visión artificial para PyTorch, diseñada para detectar y rastrear puntos clave del cuerpo humano, rostro, manos y pies en imágenes y videos. Proporciona un sistema para la estimación de posturas esqueléticas y el seguimiento de poses de múltiples personas. El proyecto implementa herramientas para la reconstrucción de poses humanas en tres dimensiones, generando posiciones de articulaciones y mallas corporales a partir de datos de imágenes bidimensionales. También incluye un rastreador de poses multi-persona capaz de mantener la identidad de varios individuos a través de fotogramas de video consecutivos. El framework cubre una amplia gama de capacidades de visión artificial, incluyendo la localización de puntos clave en múltiples personas, el seguimiento de movimiento humano y la reconstrucción de mallas corporales en 3D.

MMPose 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

This 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

OpenCVSharp 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

RF-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

Sapiens es un modelo de visión humana de alta resolución diseñado para tareas de visión artificial centradas en el ser humano de alta precisión. Funciona como un conjunto de herramientas para estimar la pose humana, la profundidad y la geometría de la superficie. El proyecto utiliza un backbone de vision transformer para realizar múltiples tareas a través de un codificador compartido. Esta arquitectura permite la predicción simultánea de estructuras esqueléticas, ubicaciones de articulaciones y la distancia entre una cámara y un sujeto humano. Las capacidades del modelo cubren la segmentación de partes del cuerpo humano para aislar regiones anatómicas de los fondos y la predicción de normales de superficie para recuperar detalles geométricos 3D a partir de imágenes 2D. Estas tareas están respaldadas por un framework de aprendizaje multitarea que emplea regresión a nivel de píxel y enmascaramiento de segmentación semántica.

Este es un framework de estimación de pose multi-persona diseñado para la detección de puntos clave humanos en tiempo real. Funciona como un estimador de pose humana de abajo hacia arriba que identifica articulaciones esqueléticas en todas las personas de una escena sin requerir un detector de personas separado. El sistema utiliza un modelo de red neuronal convolucional para generar mapas de calor y campos vectoriales para el análisis de postura. Implementa específicamente campos de afinidad de partes para codificar la ubicación y orientación de las extremidades, permitiendo al modelo conectar articulaciones individuales en esqueletos completos. El proyecto cubre el análisis de movimiento por visión artificial y el análisis de postura mediante deep learning, extrayendo coordenadas espaciales de partes del cuerpo humano a partir de datos de imagen y video.

Roboflow 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

LightGlue is a deep learning framework designed for local feature matching and high-speed correspondence estimation between pairs of images. It functions as a computer vision matching model that identifies corresponding keypoints across different viewpoints. The system utilizes an adaptive neural network architecture that dynamically optimizes inference speed by pruning its own depth and width based on the input image pairs. This approach employs a transformer-style attention mechanism and cross-image attention to compute correlations between feature descriptors. The matching process include

Este proyecto es una implementación en PyTorch de una arquitectura de investigación diseñada para el aprendizaje de representaciones de alta resolución. Funciona como un framework de visión artificial centrado en la detección precisa de puntos clave, la estimación de poses humanas y la segmentación semántica de imágenes. La implementación proporciona herramientas especializadas para identificar puntos de referencia anatómicos en el cuerpo humano y predecir coordenadas de puntos clave faciales para analizar la orientación y alineación. Utiliza un sistema de flujos paralelos de múltiples resoluciones y fusión multiescala repetida para mantener representaciones de alta resolución en toda la red. El framework abarca una amplia gama de tareas de visión artificial, incluyendo detección de objetos, clasificación de imágenes y segmentación semántica a nivel de píxel. También incluye flujos de trabajo para entrenar modelos en datasets etiquetados y evaluar cuantitativamente la precisión de las posiciones articulares predichas frente a datos de validación.

Este proyecto es un kit de herramientas de bioimagen y suite de análisis en Python, diseñado para procesar y analizar imágenes de microscopía y médicas. Proporciona una colección de herramientas para la cuantificación de imágenes, segmentación de imágenes médicas y flujos de trabajo generales de bioimagen. La suite incluye capacidades especializadas para cuantificar datos biológicos, como medir la complejidad de ramificación neuronal mediante análisis de Sholl, calcular distribuciones de tamaño de partículas y rastrear el área de heridas en ensayos de scratch. También cuenta con una librería de segmentación de imágenes médicas que implementa arquitecturas U-Net para aislar estructuras anatómicas en datos 3D y utiliza redes generativas antagónicas (GANs) para crear imágenes científicas sintéticas para el aumento de datasets. En términos generales, el proyecto cubre primitivas de procesamiento de imágenes, incluyendo reducción de ruido, mejora de contraste y transformaciones morfológicas. Proporciona utilidades de gestión de datasets para convertir anotaciones entre formatos COCO, YOLO y máscaras binarias, así como herramientas de machine learning para entrenar redes neuronales e implementar transferencia de pesos basada en autoencoders. Los flujos de trabajo de análisis se proporcionan como una serie de Jupyter Notebooks interactivos.