14 个仓库
Tools for labeling data and managing segmentation datasets.
Explore 14 awesome GitHub repositories matching part of an awesome list · Annotation and Data Tools. Refine with filters or upvote what's useful.
AirSim is a high-fidelity simulation platform designed for the development and testing of autonomous vehicles. Built as a plugin for game engines, it provides a physics-based environment that models vehicle dynamics and sensor data, serving as a foundation for robotics research, computer vision training, and reinforcement learning. The platform distinguishes itself through its support for hardware-in-the-loop and software-in-the-loop testing, allowing developers to validate control logic and firmware against real-world signals or concurrent processes. It offers extensive programmatic control
Simulation platform for data generation.
CVAT 是一个开源计算机视觉标注工具和可视化数据集管理平台。它提供了一个自托管界面,用于标注图像、视频和 3D 数据,以创建视觉 AI 模型的数据集。 该平台具有 AI 辅助数据标注功能,可自动创建掩码和边界框,并利用插件系统连接外部机器学习模型。它包括一个基于共识的质量保证系统,通过比较独立标注来验证标签准确性。 该系统涵盖协作团队管理、通过任务分解进行项目组织以及远程云存储集成。它还提供用于程序化工作流控制以及以行业标准格式导入和导出数据的 REST API。
Computer Vision Annotation Tool.
Labelme 是一个基于 Python 的图像标注工具,用于创建计算机视觉数据集。它作为语义分割的可视化编辑器,允许用户使用多边形、矩形、点和圆定义对象边界。该应用程序还可用作多光谱图像标注器,支持卫星和科学图像中使用的位深度较高的 TIFF 文件。 该工具集成了 AI 辅助标注功能,可自动创建掩码和多边形。这些功能允许通过文本提示或交互式点选择来生成形状,根据用户放置的正负点提出边界建议。 该软件涵盖了广泛的数据管理和标注任务,包括创建密集像素掩码、旋转边界框和视频帧序列。它包含一个将内部 JSON 状态持久化转换为 COCO 和 Pascal VOC 等标准数据集格式的管道。其他功能包括图像级分类标志、几何细化工具和批量图像导入。
LabelMe annotation tool in Python.
Doccano 是一个协作式标注平台和文本标注工具,旨在为机器学习创建训练数据。它提供了一个专门的界面,用于对自然语言数据集执行序列标注和文本分类。 该系统作为一个监督学习数据集管理器,允许多个用户在共享工作区内协作,为自然语言处理任务标注数据集。它通过将非结构化文档转换为结构化标注示例,支持为模型训练准备原始文本数据。 该平台包括协作数据标注、文本数据集标注和机器学习预处理功能。这些任务由用于管理标注数据和协调团队工作的 Web 界面提供支持。
Allows users to automate the organization and movement of annotation data via a programmatic API.
ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself
Exports processed datasets to external tools for manual review, cleaning, and metadata enrichment.
Source code for the LabelMe annotation tool.
LabelMe annotation tool.
JS Segment Annotator
JavaScript-based segmentation annotator.
GUI for COCO-style dataset generation.
UI for surface segmentation.