microsoft/vott 的主要功能有哪些？

microsoft/vott 的主要功能包括：Bounding Box Interfaces, Annotation Format Exporters, 2D Object Labeling, Computer Vision Annotation, Data Labeling Tools, Dataset Preparation Tools, Data Preparation Tools, Dataset Exports。

microsoft/vott 有哪些开源替代品？

microsoft/vott 的开源替代品包括： tzutalin/labelimg — labelImg is a desktop image annotation tool and dataset preparation utility used to create labeled datasets for… puzzledqs/bbox-label-tool — BBox-Label-Tool is a web-based utility designed for labeling image collections and defining spatial object boundaries… cvhub520/x-anylabeling — X-AnyLabeling is an AI-assisted annotation platform and computer vision labeling tool. It provides an interface for… wkentaro/labelme — Labelme is a Python-based image annotation tool used to create computer vision datasets. It serves as a visual editor… doccano/doccano — Doccano is a collaborative data labeling platform and machine learning dataset management system. It provides a… humansignal/labelimg — labelImg is a computer vision labeling tool and image bounding box annotator used to create training datasets for…

VoTT

VoTT 是一款计算机视觉标注软件和机器学习数据集准备工具。它是一款桌面应用程序，专为在图像和视频中绘制边界框并为对象分配标签而设计，用于创建目标检测模型的训练数据集。

该应用利用跨平台桌面界面来管理图像和视频资源。它具有本地优先的存储集成，可直接从主机文件系统处理大型媒体资源，并包括帧率控制的视频采样，以从视频流中提取特定图像进行标注。

该软件涵盖了完整的数据生命周期，包括从本地或云存储导入资源，以及通过基于模式的导出将标注数据转换为各种机器学习格式。它还结合了基于令牌的加密来保护敏感的项目配置设置。

Features

Bounding Box Interfaces - Provides a specialized graphical interface for drawing rectangular bounding boxes around objects for detection model training.
Annotation Format Exporters - Converts internal annotation coordinates into standardized machine learning dataset formats via template-based exporters.
2D Object Labeling - Provides a tool for creating rectangular bounding box annotations on 2D images and video frames for object detection.
Computer Vision Annotation - Serves as a comprehensive tool for creating high-quality annotated datasets from images and videos for vision AI.
Data Labeling Tools - Provides interfaces and workflows for annotating datasets to support supervised machine learning tasks.
Dataset Preparation Tools - Provides utilities for collecting, cleaning, and curating image and video samples for machine learning model training.
Data Preparation Tools - Includes utilities to clean, format, and organize raw media assets into structures suitable for ML model ingestion.
Dataset Exports - Exports image and video samples and their associated annotations into specific formats for AI training pipelines.
Local-First Storage - Prioritizes direct host machine file system integration to manage large media assets without requiring server uploads.
Image Labeling - Allows users to define object locations and assign tags within images to generate ground truth training data.
Video Frame Annotations - Supports tagging objects across video sequences by navigating through frames to ensure consistency for ML models.
Frame Annotation Workflows - Enables tagging of objects across video sequences by navigating through extracted frames to maintain temporal consistency.
Media Asset Importers - Connects to local file systems and cloud storage providers to import images and videos for the labeling pipeline.
Electron Desktop Applications - Implements a cross-platform desktop application using the Electron framework to integrate web technologies with native system access.
Frame Sampling Strategies - Implements specific sampling strategies to extract a sequence of labelable images from video streams at set intervals.
Canvas-Based Annotation UIs - Provides a graphical canvas interface for drawing bounding boxes and tagging objects in real-time on images and video frames.
Desktop Shells - Uses an Electron shell to wrap the web interface, enabling native desktop window management and local file system access.
Image Annotation - Cross-platform utility for labeling image and video assets.
Image Annotation Tools - Tool for labeling and annotating images for object detection models.

Star 历史

microsoftVoTTArchived

Name: microsoft/vott
Author: microsoft

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4,427 星标838 分支TypeScriptMIT5 次浏览

VoTT

Features

Bounding Box Interfaces - Provides a specialized graphical interface for drawing rectangular bounding boxes around objects for detection model training.
Annotation Format Exporters - Converts internal annotation coordinates into standardized machine learning dataset formats via template-based exporters.
2D Object Labeling - Provides a tool for creating rectangular bounding box annotations on 2D images and video frames for object detection.
Computer Vision Annotation - Serves as a comprehensive tool for creating high-quality annotated datasets from images and videos for vision AI.
Data Labeling Tools - Provides interfaces and workflows for annotating datasets to support supervised machine learning tasks.
Dataset Preparation Tools - Provides utilities for collecting, cleaning, and curating image and video samples for machine learning model training.
Data Preparation Tools - Includes utilities to clean, format, and organize raw media assets into structures suitable for ML model ingestion.
Dataset Exports - Exports image and video samples and their associated annotations into specific formats for AI training pipelines.
Local-First Storage - Prioritizes direct host machine file system integration to manage large media assets without requiring server uploads.
Image Labeling - Allows users to define object locations and assign tags within images to generate ground truth training data.
Video Frame Annotations - Supports tagging objects across video sequences by navigating through frames to ensure consistency for ML models.
Frame Annotation Workflows - Enables tagging of objects across video sequences by navigating through extracted frames to maintain temporal consistency.
Media Asset Importers - Connects to local file systems and cloud storage providers to import images and videos for the labeling pipeline.
Electron Desktop Applications - Implements a cross-platform desktop application using the Electron framework to integrate web technologies with native system access.
Frame Sampling Strategies - Implements specific sampling strategies to extract a sequence of labelable images from video streams at set intervals.
Canvas-Based Annotation UIs - Provides a graphical canvas interface for drawing bounding boxes and tagging objects in real-time on images and video frames.
Desktop Shells - Uses an Electron shell to wrap the web interface, enabling native desktop window management and local file system access.
Image Annotation - Cross-platform utility for labeling image and video assets.
Image Annotation Tools - Tool for labeling and annotating images for object detection models.

VoTT 的开源替代方案

相似的开源项目，按与 VoTT 的功能重合度排序。

tzutalin/labelimg
tzutalin/labelImg
25,012在 GitHub 上查看
labelImg is a desktop image annotation tool and dataset preparation utility used to create labeled datasets for computer vision training. It provides a graphical interface for drawing bounding boxes around objects in images and assigning them class labels to build ground truth data for machine learning models. The software specifically supports the Pascal VOC XML annotation format, exporting image coordinates and class names into standard XML or text structures. It allows users to load predefined class lists from text files to standardize naming across an entire project. Beyond initial label
Python
在 GitHub 上查看25,012
puzzledqs/bbox-label-tool
puzzledqs/BBox-Label-Tool
1,132在 GitHub 上查看
BBox-Label-Tool is a web-based utility designed for labeling image collections and defining spatial object boundaries to support supervised machine learning tasks. It provides an interface for drawing rectangular bounding boxes on images, allowing users to record coordinate data for object detection and visual recognition datasets. The tool operates entirely within the browser, utilizing local file processing to read images directly from the user's system without requiring data uploads. It maintains annotation records through browser-based storage, ensuring that spatial data persists across p
Python
在 GitHub 上查看1,132
cvhub520/x-anylabeling
CVHub520/X-AnyLabeling
8,193在 GitHub 上查看
X-AnyLabeling is an AI-assisted annotation platform and computer vision labeling tool. It provides an interface for annotating images and videos using polygons and rectangles to create training sets for machine learning models. The project distinguishes itself through the integration of external AI models via a plugin-based inference backend, allowing for automated generation of candidate labels and the execution of specialized tasks like pose estimation and object detection. It also functions as an optical character recognition tool for extracting text and layout information from document im
Pythonartificial-intelligenceclipcomputer-vision
在 GitHub 上查看8,193
wkentaro/labelme
wkentaro/labelme
15,984在 GitHub 上查看
Labelme is a Python-based image annotation tool used to create computer vision datasets. It serves as a visual editor for semantic segmentation, allowing users to define object boundaries using polygons, rectangles, points, and circles. The application also functions as a multispectral image annotator, supporting high-bit depth TIFF files used in satellite and scientific imagery. The tool incorporates AI-assisted labeling capabilities to automate the creation of masks and polygons. These features allow for shape generation driven by text prompts or interactive point selections, which propose
Python
在 GitHub 上查看15,984

查看 VoTT 的所有 30 个替代方案

常见问题解答

microsoft/vott 是做什么的？

VoTT

Features

Star 历史

VoTT

Features

VoTT 的开源替代方案

tzutalin/labelImg

puzzledqs/BBox-Label-Tool

CVHub520/X-AnyLabeling

wkentaro/labelme

常见问题解答

Star 历史

VoTT 的开源替代方案

tzutalin/labelImg

puzzledqs/BBox-Label-Tool

CVHub520/X-AnyLabeling

wkentaro/labelme

常见问题解答