Data Labelling and Annotation

Computer vision platform that helps AI teams “automate” and future-proof their training data workflows as advances in AI continue.

Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration.

Segments.ai is a labeling platform with powerful automation tools for image segmentation. It also features a flexible API and Python SDK, which enable you to quickly set up custom workflows by uploading images and labels directly from your code.

The Snorkel project started at Stanford in 2016 with a simple technical bet: that it would increasingly be the training data, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise the creators started by empowering users to programmatically label, build, and manage training data.

A complete solution for taking control of your training data with fast labeling tools, human workforce, data management, a powerful API and automation features.

Hive helps enterprises use AI to automate business processes that rely on interpreting video, image, text, or audio.

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.

A data annotation fuels your journey from exploratory R&D to proof of concept to mission-critical, production-ready solutions. We recognize that your data training process is iterative and evolving and it always as agile and flexible as you need it to be.

Label studio is an open source data labeling tool for labeling and exploring multiple types of data. You can perform many different types of labeling for many different data formats.You can also integrate Label Studio with machine learning models to supply predictions for labels (pre-labels), or perform continuous active learning.

ML data annotations made super easy for teams. Just upload data, add your team and build training/evaluation dataset in hours.

Playment is a complete data labeling platform which helps machine learning engineers build high quality ground truth datasets for training and validating machine learning models. It breaks down large problems into micro-tasks and distributes among its large community of trained annotators.

LabelMe is a project created by the MIT Computer Science and Artificial Intelligence Laboratory which provides a dataset of digital images with annotations.