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 是一个深度学习姿态估计框架和 PyTorch 计算机视觉库，旨在检测和追踪图像及视频中的人体、面部、手部和足部关键点。它提供了一套用于骨骼姿态估计和多人姿态追踪的系统。 该项目实现了三维人体姿态重建工具，可从二维图像数据生成关节位置和人体网格形状。它还包含一个多人姿态追踪器，能够在连续的视频帧中保持多人的身份识别。 该框架涵盖了广泛的计算机视觉功能，包括多人关键点定位、人体运动追踪以及三维人体网格重建。

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 是一个高分辨率人体视觉模型，专为高精度、以人为中心的计算机视觉任务而设计。它是一套用于估计人体姿态、深度和表面几何形状的工具集。 该项目利用视觉 Transformer 主干网络通过共享编码器执行多项任务。这种架构能够同时预测骨骼结构、关节位置以及相机与人体对象之间的距离。 该模型的功能涵盖了人体部位分割（从背景中分离解剖区域）和表面法线预测（从 2D 图像中恢复 3D 几何细节）。这些任务由一个采用像素级回归和语义分割掩码的多任务学习框架提供支持。

这是一个专为实时人体关键点检测设计的多人姿态估计框架。它作为自底向上的人体姿态估计器，无需单独的人员检测器即可识别场景中所有人的骨骼关节。 该系统利用卷积神经网络模型生成用于姿态分析的热图和向量场。它特别实现了部件亲和场（Part Affinity Fields）来编码肢体的位置和方向，从而允许模型将单个关节连接成完整的骨架。 该项目涵盖了计算机视觉运动分析和深度学习姿态分析，从图像和视频数据中提取人体部位的空间坐标。

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 是一个深度学习框架，专为图像对之间的局部特征匹配和高速对应估计而设计。它作为一个计算机视觉匹配模型，用于识别不同视角下的对应关键点。 该系统利用自适应神经网络架构，根据输入的图像对动态修剪自身的深度和宽度，从而优化推理速度。这种方法采用 Transformer 风格的注意力机制和跨图像注意力来计算特征描述符之间的相关性。 匹配过程包括迭代细化循环和动态提前停止机制，一旦达到置信度阈值即停止计算。这些功能支持更广泛的计算机视觉流水线，用于实时图像对齐和神经网络推理优化。

This project is a PyTorch implementation of a research architecture designed for high-resolution representation learning. It serves as a computer vision framework focused on precise keypoint detection, human pose estimation, and semantic image segmentation. The implementation provides specialized tools for identifying anatomical landmarks on the human body and predicting facial keypoint coordinates to analyze orientation and alignment. It utilizes a system of multi-resolution parallel streams and repeated multi-scale fusion to maintain high-resolution representations throughout the network.

这是一个 Python 生物成像工具包和分析套件，专为处理和分析显微镜及医学图像而设计。它提供了一系列用于图像量化、医学图像分割和通用生物成像工作流的工具。 该套件包括用于量化生物数据的专门功能，例如通过 Sholl 分析测量神经元分支复杂性、计算颗粒大小分布以及在划痕实验中跟踪伤口面积。它还具有一个医学图像分割库，实现了用于隔离 3D 数据中解剖结构的 U-Net 架构，并使用生成对抗网络创建用于数据集增强的合成科学图像。 总体而言，该项目涵盖了图像处理原语，包括去噪、对比度增强和形态学变换。它提供用于在 COCO、YOLO 和二进制掩码格式之间转换标注的数据集管理实用程序，以及用于训练神经网络和实现基于自动编码器的权重迁移的机器学习工具。 分析工作流以一系列交互式 Jupyter Notebook 的形式提供。