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 este un framework de evaluare, o bibliotecă de testare și un sistem de monitorizare a calității pentru modele de limbaj mari (LLM) și agenți AI. Acesta servește drept set de instrumente pentru cuantificarea performanței și fiabilității modelelor, oferind capabilități specializate pentru validarea pipeline-urilor de tip retrieval-augmented generation (RAG). Proiectul se distinge printr-un instrument automat de red teaming și un scaner de securitate conceput pentru a identifica vulnerabilități, prompt injections și riscuri de siguranță. Utilizează sondarea adversă și generarea sintetică de cazuri limită pentru a cuantifica robustețea modelului și a detecta scurgerile de informații. Platforma acoperă o gamă largă de capabilități, inclusiv detectarea halucinațiilor și a acurateței faptice, benchmarking-ul raționamentului și logicii, precum și detectarea bias-urilor. Oferă instrumente pentru testarea de regresie, evaluarea componentelor RAG și generarea automată a cazurilor de test din baze de cunoștințe. Sistemul include funcții de gestionare pentru spații de lucru colaborative, control al accesului bazat pe roluri și pipeline-uri de evaluare programate pentru a monitoriza degradarea performanței în timp.

Argilla este un instrument colaborativ de feedback AI și un sistem de gestionare a curării datelor. Servește ca o platformă de seturi de date human-in-the-loop concepută pentru a coordona annotatorii din forța de muncă și experții în domeniu în etichetarea, evaluarea și rafinarea mostrelor de date pentru proiecte de machine learning. Platforma se concentrează pe curarea seturilor de date pentru modele mari de limbaj și fluxuri de lucru de învățare prin consolidare din feedback uman (RLHF). Oferă un spațiu de lucru partajat pentru integrarea expertizei umane în dezvoltarea AI pentru a valida output-urile modelului și a corecta erorile de date. Sistemul gestionează pipeline-ul de date de machine learning end-to-end, inclusiv importul seturilor de date din hub-uri externe, definirea schemelor de feedback personalizate pentru etichete și ranking-uri, și exportul datelor adnotate. Suportă gestionarea programatică a datelor și crearea de fluxuri de lucru automatizate pentru a îmbunătăți iterativ performanța modelului.

Acest proiect este o implementare de deep learning a arhitecturii RetinaNet pentru detectarea și clasificarea obiectelor în imagini. Construit ca un framework de detecție a obiectelor Keras și un instrument de computer vision TensorFlow, oferă o implementare completă de rețea neuronală bazată pe lucrarea RetinaNet. Framework-ul include componente specializate precum un Feature Pyramid Network și o funcție de pierdere focală (focal loss) pentru a gestiona detecția obiectelor. Dispune de o arhitectură backbone configurabilă și bounding boxes bazate pe ancore pentru a prezice locațiile obiectelor pe diferite scări și rapoarte de aspect. Toolset-ul acoperă fluxul de lucru end-to-end pentru computer vision, inclusiv rutine de antrenare, evaluarea performanței și deployment-ul inferenței modelului. Oferă utilitare de gestionare a datelor pentru importarea și depanarea adnotărilor de imagini din formatele CSV și Pascal VOC, precum și instrumente pentru conversia modelelor antrenate în diferite formate pentru deployment.

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