JS Segment Annotator
The main features of kyamagu/js-segment-annotator are: Annotation and Data Tools, Image Annotation.
Open-source alternatives to kyamagu/js-segment-annotator include: wkentaro/labelme — Labelme is a Python-based image annotation tool used to create computer vision datasets. It serves as a visual editor… opencv/cvat — CVAT is an open-source computer vision annotation tool and visual dataset management platform. It provides a… labelbox/labelbox. puzzledqs/bbox-label-tool — BBox-Label-Tool is a web-based utility designed for labeling image collections and defining spatial object boundaries… openimages/dataset — This project is a computer vision dataset and image annotation repository designed for training and evaluating machine… openseadragon/openseadragon — OpenSeadragon is a JavaScript library and tiled image rendering engine designed for high-resolution image viewing. It…
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
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
This project is a computer vision dataset and image annotation repository designed for training and evaluating machine learning models. It provides a large collection of labeled images, serving as an object detection benchmark and a source of pixel-level segmentation data. The repository distinguishes itself as a multimodal visual dataset by pairing images with synchronized voice, text, and mouse traces to support narrative understanding. It further enables the analysis of model fairness through the inclusion of demographic attributes and exhaustive annotations. The dataset covers a broad ra