Awesome GitHub RepositoriesObject Detection

This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

LocalAI is a local generative AI platform and inference engine designed to host large language, vision, and audio models on private hardware. It functions as an API compatible gateway that mimics proprietary service endpoints, allowing existing third-party software to integrate with a self-hosted backend. The platform distinguishes itself as a distributed AI model orchestrator, capable of scaling inference across machine clusters using VRAM-aware routing and hardware coordination. It provides a unified interface for diverse open-source backends and supports self-hosted RAG infrastructure thro

This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files. The project distinguishes itself through its highly modular

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 an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit

Paper2gui is a multi-modal AI toolkit and model GUI wrapper designed to deploy and run various artificial intelligence models through a visual interface. Its primary purpose is to provide a way to execute complex AI research papers and models without requiring manual software installation or coding. The project distinguishes itself by using a wrapper-based model interface that abstracts command line arguments into visual input fields, utilizing template-driven UI generation to create parameter sliders and forms based on the specific requirements of the underlying model. It includes a centrali

This is a real-time object detection framework built on the YOLOv3 architecture, implemented in PyTorch. It provides a complete pipeline for identifying and localizing objects in images and video using a single neural network pass, combining a Darknet-53 backbone with multi-scale feature pyramids and anchor-based bounding box prediction. The framework extends beyond basic detection to include instance segmentation, human pose estimation, and multi-object tracking across video frames. It offers a model export toolkit that converts trained models through ONNX to CoreML, TensorFlow Lite, and Ten

This project is a collection of optional, community-contributed algorithms and specialized vision tools that extend the core OpenCV framework. It serves as a comprehensive library of extra modules for computer vision research, providing advanced toolsets for image processing, visual data analysis, and object detection. The library includes specialized frameworks for augmented reality tracking, biometric face recognition, and three-dimensional pose estimation. It provides distinct capabilities for identifying AR markers, tracking 3D object silhouettes, and performing neural network vulnerabili

This project is a computer control framework that uses multimodal vision models to simulate mouse and keyboard inputs for automating desktop tasks. It functions as an autonomous agent and vision-based orchestrator that interprets screen visuals to interact with user interfaces. The system employs vision language models and object detection to locate and click interface elements. It utilizes visual grounding to overlay numerical markers on UI components and uses optical character recognition to map on-screen text to precise pixel coordinates. The framework supports voice-controlled computing

AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi

Moondream is a small-scale vision language model designed to reason across images to generate captions and answer natural language questions. It functions as an edge-optimized system capable of performing visual question answering, image captioning, and object detection. The project distinguishes itself through a lightweight architecture designed for local inference on embedded devices, workstations, and air-gapped hardware. It supports the execution of models on local GPUs and Apple Silicon to ensure data privacy and low latency. The system's capabilities include identifying precise object

GroundingDINO is a deep learning vision model and open-vocabulary object detector designed to map natural language prompts to spatial coordinates. It functions as a text-to-bounding-box framework that enables zero-shot image localization, allowing the system to identify and locate arbitrary objects without requiring predefined classes or specific training for those categories. The project distinguishes itself by matching visual features to natural language descriptions to achieve open-set visual recognition. It supports text-guided image localization and the isolation of specific objects base

This project is a Python implementation of the Faster R-CNN object detection framework. It serves as a convolutional neural network library and tool for locating and classifying multiple objects within images. The framework provides a pre-trained model implementation that allows for object detection inference without manual training. It supports the full lifecycle of object detection, including training detectors on visual datasets to identify and bound specific object classes. The system covers capabilities for computer vision model evaluation, neural network optimization to reduce model si

LibrePhotos is a self-hosted photo management system designed for organizing, tagging, and searching photos and videos stored on personal servers. It functions as an AI-powered media organizer and a multi-user media server, providing isolated accounts and secure permissions so multiple people can manage private collections on a single system. The project differentiates itself through a semantic image search engine that allows for natural language queries based on deep metadata analysis. It incorporates computer vision to automatically detect faces and identify objects, while utilizing geospat

Este proyecto es un framework de detección de objetos de PyTorch que implementa la arquitectura Faster R-CNN. Sirve como modelo de visión para predecir cuadros delimitadores precisos alrededor de múltiples objetos dentro de imágenes y transmisiones de video en vivo. El sistema está optimizado para el entrenamiento multi-GPU para reducir el tiempo requerido para la convergencia del modelo. Utiliza un diseño acelerado por GPU para manejar el entrenamiento y la inferencia de redes de detección complejas. El framework cubre el ciclo de vida completo de la detección de objetos, incluyendo el entrenamiento de redes personalizadas y la inferencia para imágenes estáticas y transmisiones de video en tiempo real. Incluye capacidades para la validación del rendimiento del modelo utilizando conjuntos de datos estandarizados, así como optimizaciones de entrenamiento como el agrupamiento basado en relación de aspecto y cargas de trabajo distribuidas.

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

This is an open-source autonomous driving perception pipeline that processes camera and lidar sensor data to detect, track, and fuse objects in real-world driving environments. The project integrates an end-to-end perception workflow combining sensor calibration, deep learning object detection, Kalman filter tracking, and sensor fusion for robust scene understanding. The pipeline includes camera calibration tools to remove lens distortion from raw images, deep learning model training for object classification and detection, and multi-object tracking using Kalman filters with data association

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

yolotf es un framework de detección de objetos que proporciona herramientas para convertir configuraciones y pesos de modelos de Darknet a grafos de TensorFlow. Incluye un entrenador de modelos de TensorFlow para entrenar nuevos modelos de detección o ajustar pesos existentes utilizando datasets personalizados. El proyecto cuenta con un exportador de modelos móviles que serializa definiciones de grafos y metadatos en archivos protobuf para su despliegue en dispositivos móviles. El framework soporta inferencia de detección de objetos en imágenes y video para identificar objetos y exportar coordenadas de cajas delimitadoras. Gestiona el estado del modelo mediante la traducción de mapeo de pesos y entrenamiento basado en puntos de control para permitir la restauración de pesos y estados del optimizador.