Awesome GitHub RepositoriesData Visualization Tools

Twenty is a headless customer relationship management framework that enables developers to build, version, and deploy custom business applications using code. By utilizing a declarative approach to data modeling, the platform allows for the definition of custom objects, fields, and complex relationships directly within the source code. This schema-driven architecture automatically generates corresponding REST and GraphQL APIs, ensuring that data structures and interface components remain synchronized across development and production environments. The platform distinguishes itself through a m

Metabase is a business intelligence platform designed to connect to various storage systems and relational databases for data exploration, visualization, and reporting. It provides a centralized environment where users can build queries through a graphical interface or raw code, transforming raw information into interactive dashboards and charts. The platform is built to support self-service analytics, allowing non-technical team members to extract insights without requiring deep knowledge of database syntax. The platform distinguishes itself through a metadata-driven modeling layer that abst

Dokploy is a self-hosted platform-as-a-service designed to simplify the deployment and management of containerized applications and databases. It provides a centralized control plane that decouples administrative management from application workloads, allowing users to oversee infrastructure across multiple server nodes through a unified web interface or a command-line tool. The platform distinguishes itself through an extensive library of pre-configured application templates, enabling the rapid deployment of databases, identity providers, and various productivity or development tools. It sup

This project is a curated directory of resources, extensions, and themes designed to extend the functionality of the Visual Studio Code editor. It serves as a comprehensive index for developers seeking to enhance their coding environment, offering a structured collection of community-driven tools that streamline development workflows and improve editor productivity. The directory distinguishes itself by organizing a vast ecosystem of plugins into logical categories, ranging from language-specific intelligence and version control integrations to advanced productivity utilities. It highlights t

This application is a desktop utility for managing, editing, and visualizing local database files. It provides a graphical interface for executing SQL queries, designing database structures, and performing routine maintenance tasks on data stores. The software distinguishes itself through its support for encrypted database files, allowing users to manage password-protected data using modular cryptographic extensions. It also offers built-in tools for data analysis, enabling the generation of graphical charts and plots directly from query results to identify trends within datasets. Beyond its

Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea

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

ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts. The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and

ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving. The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r

charts.css is a CSS-driven framework designed to transform semantic HTML into accessible data visualizations without relying on JavaScript. It functions as a charting library that uses standard HTML structures, such as tables and lists, to render graphs while maintaining full compatibility with screen readers. The project distinguishes itself by using CSS variables to map numeric data to visual dimensions and utility classes to control chart types and layouts. It supports a wide range of visual styles, including 3D effects, reflection effects, and customized color palettes integrated via a br

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate

vue-chartjs is a component library that wraps Chart.js chart types as reusable Vue components, integrating the charting library into Vue's component model. It provides reactive props binding so that charts automatically redraw when data or options change, and exposes the underlying Chart.js instance via template refs for direct programmatic access. The library also forwards Chart.js interaction events like clicks and hovers to Vue component event handlers, and applies ARIA labels to chart elements for screen reader support. The library distinguishes itself by offering typed Vue components for

Giskard es un framework de evaluación, librería de pruebas y sistema de monitoreo de calidad para modelos de lenguaje grandes (LLM) y agentes de IA. Sirve como un kit de herramientas para cuantificar el rendimiento y la fiabilidad del modelo, proporcionando capacidades especializadas para validar pipelines de generación aumentada por recuperación (RAG). El proyecto se distingue por una herramienta de red teaming automatizada y un escáner de seguridad diseñado para identificar vulnerabilidades, inyecciones de prompts y riesgos de seguridad. Utiliza sondeo adversarial y generación sintética de casos límite para cuantificar la robustez del modelo y detectar la divulgación de información. La plataforma cubre una amplia gama de capacidades, incluyendo la detección de precisión factual y alucinaciones, benchmarking de razonamiento y lógica, y detección de sesgos. Proporciona herramientas para pruebas de regresión, evaluación de componentes RAG y la generación automatizada de casos de prueba a partir de bases de conocimiento. El sistema incluye funciones de gestión para espacios de trabajo colaborativos, control de acceso basado en roles y pipelines de evaluación programados para monitorear la deriva del rendimiento a lo largo del tiempo.

Argilla es una herramienta de retroalimentación de IA colaborativa y sistema de gestión de curación de datos. Sirve como una plataforma de conjuntos de datos human-in-the-loop diseñada para coordinar anotadores de fuerza laboral y expertos en el dominio en el etiquetado, calificación y refinamiento de muestras de datos para proyectos de aprendizaje automático. La plataforma se centra en la curación de conjuntos de datos para modelos de lenguaje grandes y flujos de trabajo de aprendizaje por refuerzo a partir de retroalimentación humana (RLHF). Proporciona un espacio de trabajo compartido para integrar la experiencia humana en el desarrollo de IA para validar las salidas del modelo y corregir errores de datos. El sistema gestiona el pipeline de datos de aprendizaje automático end-to-end, incluyendo la importación de conjuntos de datos desde hubs externos, la definición de esquemas de retroalimentación personalizados para etiquetas y clasificaciones, y la exportación de datos anotados. Admite la gestión programática de datos y la creación de flujos de trabajo automatizados para mejorar iterativamente el rendimiento del modelo.

Este proyecto es una implementación de deep learning de la arquitectura RetinaNet para detectar y clasificar objetos dentro de imágenes. Construido como un framework de detección de objetos Keras y una herramienta de visión por computadora TensorFlow, proporciona una implementación completa de red neuronal basada en el paper de RetinaNet. El framework incluye componentes especializados como una red de pirámide de características (Feature Pyramid Network) y una función de pérdida focal para manejar la detección de objetos. Cuenta con una arquitectura de backbone configurable y cajas delimitadoras basadas en anclas para predecir ubicaciones de objetos a través de escalas y relaciones de aspecto variables. El conjunto de herramientas cubre el flujo de trabajo de extremo a extremo para visión por computadora, incluyendo rutinas de entrenamiento, evaluación de rendimiento y despliegue de inferencia de modelos. Proporciona utilidades de gestión de datos para importar y depurar anotaciones de imágenes desde formatos CSV y Pascal VOC, así como herramientas para convertir modelos entrenados en diferentes formatos para su despliegue.

This project is a Model Context Protocol server that enables large language models to generate and render data visualizations, charts, and diagrams. It functions as a toolset for AI assistants to transform raw data into professional visual representations. The server utilizes an intelligent selection layer to determine the most effective visualization format based on the provided data. It supports remote rendering via external HTTP services and provides the flexibility to route requests to self-hosted rendering endpoints for private network environments. Capabilities cover a wide range of da