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Software that allows you to manually and quickly annotate images in directories. The method is pseudo manual because it uses the algorithm watershed marked of OpenCV. The general idea is to manually provide the marker with brushes and then to launch the algorithm. If at first pass the segmentation needs to be corrected, the user can refine the markers by drawing new ones on the erroneous areas (as shown on video below). Donating is very simple - and secure. Please click here to make a donation.
Try it out at udt.dev, download the desktop app or run on-premise. The Universal Data Tool is a web/desktop app for editing and annotating images, text, audio, documents and to view and edit any data defined in the extensible .udt.json and .udt.csv standard.
doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence to sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on. Just create a project, upload data and start annotating. You can build a dataset in hours. You can try the annotation demo.
CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.
Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.
Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.
This repository contains a collection of recipes for Prodigy, our scriptable annotation tool for text, images and other data. In order to use this repo, you'll need a license for Prodigy – see this page for more details. For questions and bug reports, please use the Prodigy Support Forum. If you've found a mistake or bug, feel free to submit a pull request. ✨ Important note: The recipes in this repository aren't 100% identical to the built-in recipes shipped with Prodigy. They've been edited to include comments and more information, and some of them have been simplified to make it easier to follow what's going on, and to use them as the basis for a custom recipe.
The Panoramic Graph Environment Annotation toolkit, abbreviated as PanGEA, is a lightweight and customizable codebase for collecting audio and text annotations in panoramic graph environments, such as Matterport3D and StreetLearn. PanGEA has been used to collect the RxR dataset of multilingual navigation instructions, and to perform human wayfinding evaluations of machine-generated navigation instructions. The src directory contains the core components used to create a plugin.
Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitating the creation and management of annotations. DAML project team welcomes contributions from the community. For more detailed information, see CONTRIBUTING.md.
This repo contains a JupyterLab extension for Prodigy, our scriptable annotation tool for creating training data for machine learning models. It lets you run Prodigy within a JupyterLab tab, and annotate as you develop your models and applications. In order to use this extension, you'll need a license for Prodigy – see this page for more details. For questions, please use the Prodigy Support Forum. If you've found a bug, feel free to submit a pull request. To use this extension, you need JupyterLab >= 2.0.0 ⚠️ and Prodigy.