Awesome GitHub RepositoriesEdge Object Detection

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in

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

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

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

Dieses Projekt bietet eine Suite leichtgewichtiger Gesichtserkennungsmodelle, die für die Inferenz mit hoher Geschwindigkeit auf Edge-Computing-Geräten entwickelt wurden. Es basiert auf einer kompakten neuronalen Netzwerkarchitektur, die die Erkennung menschlicher Gesichter in Umgebungen mit begrenzten Rechenressourcen und Leistungsbeschränkungen ermöglicht. Das System bietet quantisierte Gesichtserkenner in mehreren Formaten, um die Kompatibilität über verschiedene Hardwarearchitekturen hinweg sicherzustellen. Es enthält Dienstprogramme für den Modellexport und die Quantisierung, wodurch trainierte Gewichte in standardisierte Formate für hardwareunabhängige Bereitstellungen konvertiert werden können. Das Projekt deckt Workflows für das Training benutzerdefinierter Modelle und die Verarbeitung von Bilddatensätzen ab, um Präzisions- und Geschwindigkeitsprofile anzupassen. Es unterstützt Echtzeit-Computer-Vision-Aufgaben durch die Verwendung von Integer-only-Arithmetik und einer C-basierten Inferenz-Runtime, um den Overhead auf eingebetteter Hardware zu minimieren.

YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images and video based on free-form text prompts without requiring predefined category labels. The system enables the identification of arbitrary objects by fusing image features with text embeddings. It includes a specialized tool for automated image labeling, which generates bounding box annotations for custom datasets using text-based prompts. The project provides a deployment pipeline for converting models into quantized ONNX and TFLite formats, supporting real-time inference on

NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

PaddleX is a PaddlePaddle-based framework for building, deploying, and fine-tuning AI model pipelines, with pre-built support for computer vision, OCR, document analysis, and time series tasks. It offers a toolkit of ready-to-use pipelines for image classification, object detection, segmentation, and pose estimation, alongside an end-to-end OCR document analysis pipeline that extracts text, tables, formulas, and layout information. The platform also includes a dedicated time series forecasting pipeline for analyzing historical data to detect anomalies, classify patterns, and predict future val

This is a webcam-based client for a local llama.cpp server that enables real-time object detection and vision-language model inference directly from a browser. It captures frames from the user's webcam at configurable intervals and sends them to a locally running inference server for analysis, displaying both detection results and textual scene descriptions as they are produced. The application distinguishes itself by combining object detection with vision-language scene description in a single real-time interface, all processed through a local llama.cpp server for private, offline operation.

lite.ai.toolkit ist ein C++ Computer-Vision-Toolkit für Edge-KI-Deployments. Es ermöglicht die Ausführung vortrainierter Modelle für Objekterkennung, Bildklassifizierung und Segmentierung auf ressourcenbeschränkten Geräten. Das Projekt bietet eine Multi-Backend-Inferenz-Engine, die die ONNX-Model-Runtime unterstützt, wodurch KI-Modelle auf verschiedenen Hardware-Zielen ausgeführt werden können. Es enthält eine GPU-beschleunigte Pipeline speziell für NVIDIA-Hardware, um Latenzen zu reduzieren und die Verarbeitungsgeschwindigkeit zu erhöhen. Das Toolkit deckt ein breites Spektrum an Funktionen zur Gesichtsanalyse ab, einschließlich Emotionserkennung, Geschlechts- und Altersschätzung sowie Kopfhaltungserkennung. Es bietet zudem Tools für die Gesichtserkennung durch die Extraktion von Feature-Embeddings und die Berechnung der Kosinus-Ähnlichkeit zur Identitätsprüfung. Zusätzliche Funktionen umfassen Image-Matting zur Vordergrundisolierung, Kolorierung von Graustufenbildern und künstlerischen Style-Transfer.

RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface

TengineKit is a mobile computer vision software development kit designed for real-time inference on local hardware. It functions as a neural network engine that executes deep learning models directly on mobile devices, enabling applications to perform complex visual analysis without relying on cloud connectivity. The framework provides specialized tools for detecting and tracking human features, including faces, hands, bodies, and irises, alongside general object detection capabilities. By utilizing a native core runtime and hardware-accelerated execution, the library processes visual data lo

This project provides a compact neural network architecture designed for human face detection and facial landmark localization. It functions as a specialized computer vision tool that identifies faces and extracts five specific facial key points within a single inference pass, making it suitable for integration into resource-constrained environments. The system utilizes a lightweight convolutional backbone and an anchor-based detection mechanism to maintain a small memory footprint while performing real-time processing. By employing a multi-task learning head, the model simultaneously predict