Awesome GitHub RepositoriesCoordinate Normalization Utilities

OmniParser is a multimodal interaction engine designed to function as a desktop automation agent. It interprets visual screen information to execute complex, multi-step tasks across operating system environments by bridging visual interface perception with language models. Through a continuous cycle of observation and command execution, the system grounds high-level natural language instructions into precise, coordinate-based actions. The project distinguishes itself by utilizing vision-based parsing to interact with software interfaces without requiring access to underlying application progr

LivePortrait is a computer vision framework designed for portrait animation and generative video synthesis. It functions as a deep learning system that transfers facial expressions and head movements from a driving video source onto a static image or an existing portrait video, effectively decoupling the subject's identity from the dynamic motion patterns. The framework utilizes keypoint-based motion retargeting and implicit 3D latent representations to map movements across different subjects, including both human and animal portraits. By employing canonical motion normalization and feature-s

Claude Quickstarts is a development framework and collection of reference implementations designed for building autonomous agents. It provides the foundational patterns necessary to orchestrate multi-agent workflows, enabling models to perform complex, multi-step tasks across software engineering, customer support, and computer-use domains. The platform distinguishes itself through specialized capabilities for desktop and browser automation, allowing agents to interact with graphical interfaces by capturing visual context and executing precise mouse and keyboard inputs. It includes robust inf

Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov

UI-TARS is an LLM GUI automation framework and multimodal action grounding system. It functions as a GUI agent orchestrator and cross-platform device controller that uses large language models to interpret graphical interfaces and execute actions across desktop and mobile operating systems. The system translates model-generated coordinates into precise screen positions to interact with visual user interface elements. It employs a multimodal approach to interpret screen layouts and decomposes complex goals into multi-step trajectories through reasoning and error correction. The project provid

Dieses Projekt ist ein groß angelegter Datensatz handgezeichneter Skizzen, der Millionen von zeitgestempelten Vektorgrafiken und Bitmaps für das Training von Machine-Learning-Modellen bereitstellt. Er dient als Trainingskorpus für Computer Vision und als Datensatz für neuronale Netze, bestehend aus kategorisierten menschlichen Skizzen, die zur Entwicklung von Bildklassifizierungs- und Erkennungsalgorithmen verwendet werden. Der Datensatz ist als Vektorgrafik-Korpus mit Strich-für-Strich-Sequenzen und Metadaten sowie als verarbeitete numpy-Arrays verfügbar. Diese Ressourcen unterstützen die Entwicklung von Zeichen-Klassifikatoren und die Untersuchung menschlicher Zeichenmuster. Die Daten werden in mehreren Formaten bereitgestellt, darunter rohe Vektordaten in newline-delimited JSON, normalisierte Vektorsequenzen und Graustufen-Bitmaps. Er umfasst Funktionen für kategorienbasierte Partitionierung und Koordinatenskalierung, um Konsistenz über verschiedene Stichproben hinweg zu gewährleisten.

This project is an object detection evaluation library and benchmarking tool designed to calculate precision, recall, and average precision for computer vision models. It provides a suite of utilities for parsing bounding box coordinates from text files and calculating spatial overlap to determine detection accuracy. The toolkit features a command line interface for comparing ground truth files against model predictions. It includes a precision-recall curve generator to visualize the relationship between precision and recall across different confidence thresholds and an intersection over unio