Open-source alternatives to Coco Annotator

To speedup and simplify image labeling/ annotation process with multiple supported formats.

OpenLabeler is an open source desktop application for annotating objects for AI appplications

Curate, Annotate, and Manage Your Data in LightlyStudio.

Free to use online tool for labelling photos. https://makesense.ai

The open platform for image labeling Try it now » Explore docs · Report Bug · Request Feature · Join the Community

An open source online platform for collaborative image labeling

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](https://img.shields.io/lgtm/grade/python/g/scalabel/scalabel.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/scalabel/scalabel/context:python)

Add image annotation functionality to any web page with a few lines of JavaScript.

A collaborative tool for labeling image data for yolo

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

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

OpenSeadragon is a JavaScript library and tiled image rendering engine designed for high-resolution image viewing. It functions as a deep zoom image viewer that renders massive images using a tiled pyramid approach, enabling smooth panning and zooming without requiring the full image file to be loaded. The project distinguishes itself through broad support for standardized image retrieval protocols, including the International Image Interoperability Framework (IIIF), IIPImage, Iris, and OpenStreetMap. It provides a hardware-accelerated rendering layer via WebGL to apply real-time filters and

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Label Studio is a multi-type data labeling tool and data annotation workspace designed to prepare datasets for machine learning training. It functions as a cloud-integrated data pipeline that imports raw data from storage, manages the annotation process, and exports labels into standardized formats. The platform features a machine learning model integration framework that connects to external model servers. This enables model-assisted annotation and active learning, allowing the system to perform pre-labeling and refine predictions based on human feedback. The software provides project manag

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

Images to inference with no labeling (use foundation models to train supervised models).

EVA: Video Annotation Tool

Fast and efficient BBox annotation for your images in YOLO, and now, VOC/COCO formats!

Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting label errors and increasing overall dataset quality. It implements a confident learning framework that iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples. The project provides specialized utilities for active learning optimization, allowing for the selection of the most impactful examples for labeling or re-labeling. It also includes an outlier detection tool to identify atypical data poin

Synthetic data generators for structured and unstructured text, featuring differentially private learning.

FIAT enables image data annotation, data augmentation, data extraction, and result visualisation/validation.

Web labeling tool for bitmap images and point clouds

Label Studio is a multi-modal data annotation platform designed to create and manage high-quality training datasets for machine learning. It functions as a self-hosted, containerized environment that supports secure, private deployments, including air-gapped configurations. The platform provides a centralized workspace for labeling diverse media types, such as images, text, audio, and time-series data, to support supervised and reinforcement learning workflows. The platform distinguishes itself through deep integration with machine learning backends, enabling active learning loops, automated

A multi-purpose Video Labeling GUI in Python with integrated SOTA detector and tracker