What are the main features of cloud-annotations/cloud-annotations?

The main features of cloud-annotations/cloud-annotations are: Computer Vision Tools, Machine Learning Data Preparation, Collaborative Annotation, Real-time Sync Engines, Data Labeling Tools, Vision Dataset Preparation, Interactive Canvas Drawing, Image Annotation Tools.

Cloud Annotations - توسيم الصور لرؤية الحاسوب

Cloud Annotations هي منصة قائمة على الويب مصممة للتعليق التوضيحي التعاوني للصور وإعداد مجموعات بيانات رؤية الحاسوب. توفر واجهة للفرق لرسم مربعات الإحاطة والمضلعات فوق الوسائط الرقمية، مما يحول الصور الخام إلى بيانات تدريب مهيكلة لنماذج تعلم الآلة.

تتميز المنصة بمحرك مزامنة في الوقت الفعلي يسمح لعدة مستخدمين بتحرير نفس الصورة في وقت واحد. من خلال استخدام التخزين المحلي القائم على المتصفح وتسلسل البيانات القياسي، تدعم سير العمل في وضع عدم الاتصال وتضمن بقاء التعليقات التوضيحية المصدرة متوافقة مع خطوط أنابيب تدريب تعلم الآلة الشائعة.

يدير التطبيق البيانات المرئية المعقدة من خلال معمارية حالة مركزية وتقنيات عرض عالية الأداء. تسهل هذه القدرات وضع تسميات بيانات متسقة عبر الفرق الموزعة، مما يبسط إنشاء مجموعات البيانات لمهام اكتشاف الكائنات وتصنيفها.

Features

Computer Vision Tools - Serves as a collaborative platform for annotating images to create structured datasets for computer vision model training.
Machine Learning Data Preparation - Provides tools for drawing bounding boxes and polygons to transform raw images into structured training data for machine learning.
Collaborative Annotation - Enables distributed teams to work together in a shared environment for consistent and efficient image labeling.
Real-time Sync Engines - Implements a real-time synchronization engine using WebSockets to allow multiple users to edit the same image simultaneously.
Data Labeling Tools - Offers a web-based interface for labeling visual media to assist in the development and categorization of computer vision models.
Vision Dataset Preparation - Facilitates the preparation of labeled image datasets for object detection and classification model training.
Interactive Canvas Drawing - Provides high-performance drawing of bounding boxes and polygons directly on an HTML5 canvas for real-time user interaction.
Image Annotation Tools - Provides a collaborative utility for teams to annotate digital images and export structured data for artificial intelligence systems.
Redux State Management - Centralizes application state in an immutable Redux store to maintain consistency across collaborative user sessions.
IndexedDB Offline Storage - Uses IndexedDB for local browser storage to enable offline annotation workflows and reduce server latency.

opencv/cvat

16,086عرض على GitHub

CVAT is an open-source computer vision annotation tool and visual dataset management platform. It provides a self-hosted interface for labeling images, videos, and 3D data to create datasets for vision AI models. The platform features AI-assisted data labeling to automate the creation of masks and bounding boxes, utilizing a plug-in system to connect external machine learning models. It includes a consensus-based quality assurance system that verifies label accuracy by comparing independent annotations. The system covers collaborative team management, project organization through task decomp

puzzledqs/BBox-Label-Tool

1,132عرض على GitHub

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

doccano/doccano

10,674عرض على GitHub

Doccano is a collaborative data labeling platform and machine learning dataset management system. It provides a web-based interface for teams to import raw text, mark datasets, and export structured annotations for model training. The project specifically supports text annotation for classification and named entity recognition tasks. It enables teams to coordinate multiple users on a single project to maintain consistent labeling guidelines and increase the speed of dataset creation. The system includes tools for data management and team coordination, providing the ability to import raw data

tzutalin/labelImg

25,012عرض على GitHub

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

cloud-annotationscloud-annotations

Cloud Annotations

Features