Projects sharing features with Human

GoCV is a computer vision library and Go language binding for OpenCV. It serves as an image processing toolkit and deep learning inference engine, providing programmatic access to a wide range of algorithms for image manipulation, object detection, and video analysis. The project differentiates itself through high-performance native bindings and hardware acceleration. It utilizes a foreign function interface to map Go calls to C++ functions and includes a hardware-agnostic backend dispatch to route neural network tasks to computation engines such as CUDA and OpenVINO. The library covers a br

CompreFace is a facial recognition system designed for human face detection, identification, and biometric identity verification. It provides a registry of known people and the ability to match faces in images against this database to determine a specific identity. The system extracts facial landmarks to map geometry and analyzes physical attributes including age, gender, and head pose. It can also verify whether two different images belong to the same individual. The project is implemented as a microservice-based deployment utilizing a REST API gateway and a PostgreSQL metadata store. It in

tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

face-api.js is a TensorFlow.js face recognition library and browser-based computer vision API. It provides tools for performing face detection, recognition, and landmark prediction within browsers and Node.js. The library includes a biometric identity descriptor generator that creates numerical vectors to compare identity and similarity between images. It features a facial landmark detection tool for mapping sixty-eight specific coordinate points on a face, as well as an age and gender estimation model. Its capabilities cover real-time facial analysis, including the recognition of facial exp

facenet-pytorch is a facial recognition library for PyTorch that provides pretrained neural networks for detecting faces and extracting facial embeddings. It includes an MTCNN face detector for locating faces and landmarks, alongside an InceptionResnet face encoder to convert facial images into high-dimensional vectors for identity verification. The project provides tools for identity recognition by comparing facial embeddings using cosine similarity. It also supports facial video tracking to maintain identity consistency across consecutive frames and allows for the fine-tuning of pretrained

clmtrackr is a JavaScript computer vision library designed for facial landmark detection and real-time tracking. It implements Constrained Local Models to identify specific coordinate points on a human face within video feeds or static images. The project functions as a real-time face warping engine and expression analysis tool. It can distort facial images via parametric models to create caricatures or identify and label emotional states such as happiness, sadness, anger, and surprise based on feature coordinates. The library covers a broad range of capabilities including automatic and manu

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

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

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 computer vision system designed for real-time facial recognition and identity tracking using live camera feeds. It provides a framework for capturing, registering, and identifying multiple individuals simultaneously by comparing live video input against a local database of pre-registered facial descriptors. The system distinguishes itself through a performance-oriented processing pipeline that balances computational load during live analysis. By combining deep neural network feature extraction with centroid-based object tracking, the software maintains consistent identity la

Face-recognition.js is a computer vision software development kit for Node.js that provides tools for detecting, mapping, and identifying human faces within images and video streams. It functions as a bridge to high-performance native libraries, enabling developers to perform complex facial analysis tasks directly within JavaScript and TypeScript environments. The library distinguishes itself by combining deep learning inference with geometric landmark mapping. It utilizes pre-trained neural networks to extract facial feature vectors and employs Euclidean distance calculations to determine th

FaceNet is a facial recognition framework designed to transform facial images into high-dimensional numerical embeddings for identity verification and recognition. It provides a deep learning face embedder that maps facial features into a Euclidean space where distance corresponds to facial similarity. The system includes tools for both supervised and unsupervised identity management. It features a face identity classifier for categorizing images into known identity classes and an unsupervised clustering tool to group similar facial embeddings together without predefined labels. The framewor

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

ImageAI is a Python computer vision library providing a suite of tools for image classification, object detection, and video analytics. It functions as an integrated framework for locating and labeling objects in static images and video streams, utilizing deep learning models for identification and categorization. The project includes a model training toolkit that allows for the creation of custom classifiers and detectors through scratch training or transfer learning. It features a GPU-accelerated inference engine to increase processing speed for vision tasks and includes specialized utiliti

This is a Python facial recognition library designed to detect, encode, and identify human faces in images and video. It functions as a biometric identification tool that converts facial features into numerical encodings to compare and match identities. The library provides a computer vision command line interface for batch processing face detection and recognition tasks across image directories. It also supports a GPU accelerated vision API that utilizes CUDA and NVIDIA hardware to increase the speed of facial analysis and identification. Its capabilities cover human face detection and faci

opencv4nodejs is a set of JavaScript wrappers and a C++ native addon that provides Node.js bindings for the OpenCV library. It functions as a computer vision library and image processing framework, exposing high-performance C++ algorithms to a JavaScript environment. The project enables the execution of vision algorithms for detecting faces, tracking objects, and analyzing visual data using deep neural networks. It includes capabilities for data pattern classification, text pattern recognition, and the identification of facial landmarks and gestures. The framework covers a broad capability s

ccv is a computer vision library written in C designed for high-performance visual analysis. It serves as a framework for image classification, object detection, and the identification of faces, pedestrians, and vehicles. The library distinguishes itself through hardware-accelerated vision and deep learning inference optimizations. It utilizes a quantized tensor processor to transform floating-point data into eight-bit integers and implements integer-quantized attention mechanisms to reduce memory bandwidth and increase data throughput. The project covers a broad range of capabilities, inclu

This project is a computer vision-based security platform designed for real-time property surveillance and automated threat detection. It integrates live video stream management with neural network processing to monitor, track, and identify individuals within a monitored environment. The system distinguishes itself through local identity management, which allows users to train classifiers on specific individuals and store facial embeddings directly on the local system. By combining this biometric analysis with event-driven logic, the platform can differentiate between known persons and unknow

jeelizFaceFilter is a browser-based computer vision engine and WebGL face tracking library designed for AR filters and real-time facial movement tracking. It functions as a neural network face detector that identifies multiple faces and monitors mouth movements and rotation within a web browser. The system distinguishes itself through a model-swappable detection pipeline, allowing the exchange of neural network weights to balance accuracy and performance across different camera angles and devices. It features real-time lighting synchronization to match the illumination of 3D overlays with the

This project is a collection of reference implementations and demonstration projects covering computer vision, DevOps automation, distributed systems, and Java-based microservices. It provides a programming reference library and practical examples for building server-side applications, containerizing services, and orchestrating clusters. The repository features a comprehensive toolset for DevOps automation, including scripts and playbooks for CI/CD pipelines and automated cluster installation. It includes a computer vision demo project for image object detection and facial analysis, as well a

VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language instructions into physical navigation waypoints and robotic actions. It functions as a multimodal policy optimizer and an open vocabulary detector capable of locating objects based on arbitrary natural language descriptions. The system distinguishes itself through the use of chain-of-thought reasoning and reinforcement learning to solve complex visual and spatial tasks. It utilizes a video semantic memory system, which employs a visual cache to maintain a history of live video for

OpenCat-Quadruped-Robot is a development framework and motion control API for building four-legged robots. It provides a comprehensive environment for quadruped robot development, featuring tools for locomotion gait design, inverse kinematics, and a layered control architecture that separates high-level intelligence from low-level motion. The project distinguishes itself as an embedded AI deployment tool, allowing users to train, quantize, and deploy machine learning models to vision modules for tasks such as object detection and visual target tracking. It further supports complex human-robot

BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package, deploy, and scale AI models as production-ready REST APIs. It functions as an AI model lifecycle manager and an inference graph orchestrator, enabling the chaining of multiple models and custom logic into complex pipelines for advanced task sequences. The framework distinguishes itself through a dynamic batching engine that optimizes GPU throughput and an artifact-based packaging system that bundles model weights and dependencies into immutable archives for consistent deployment. It

SAHI is a sliced inference framework and computer vision pipeline designed to detect small objects in high-resolution images. It provides a system for dividing large images into overlapping patches to prevent the detail loss that typically occurs during standard model downscaling, alongside an image tiling utility and a COCO dataset toolkit. The project distinguishes itself by offering a model-agnostic prediction wrapper that standardizes different machine learning frameworks into a unified interface. This allows it to implement sliced inference and object detection across various model backe

Openface is a deep learning toolkit designed for facial recognition and identity verification. It provides a comprehensive pipeline for detecting faces, aligning landmarks, and transforming facial images into compact numerical vectors. By utilizing these embeddings, the system enables identity classification and similarity comparison through geometric distance calculations. The project distinguishes itself by integrating research-oriented diagnostic tools alongside its core recognition capabilities. It includes utilities for visualizing high-dimensional feature clusters, inspecting internal c