Encord

Encord Active is an open-source toolkit for active learning in data labeling. It provides a variety of tools and features to help users label data more efficiently and effectively.

Encord Active includes the following features:

• Data sampling: Encord Active provides a variety of data sampling strategies to help users select the most important data to label. This can help users to save time and improve the quality of their labeled data.
• Model-based sampling: Encord Active can use a pre-trained model to identify the most informative data to label. This can help users to improve the performance of their models more quickly.
• Label quality control: Encord Active provides a variety of tools to help users ensure the quality of their labeled data. This includes the ability to review labels, identify outliers, and track labeler performance.
• Collaboration: Encord Active supports collaboration between multiple labelers. This can help users to label large datasets more efficiently.

Encord Active is still under development, but it has already been used by a number of organizations to improve the efficiency and effectiveness of their data labeling workflows.

Here are some of the benefits of using Encord Active:

• Save time: Encord Active can help users to save time by selecting the most important data to label and by providing tools to ensure the quality of labeled data.
• Improve model performance: Encord Active can help users to improve the performance of their models more quickly by identifying the most informative data to label.
• Increase efficiency: Encord Active supports collaboration between multiple labelers, which can help users to label large datasets more efficiently.
• Open source: Encord Active is an open-source toolkit, which means that it is free to use and modify.

Overall, Encord Active is a valuable tool for organizations that are looking to improve the efficiency and effectiveness of their data labeling workflows. It is easy to use and provides a variety of features that can help users to save time, improve model performance, and increase efficiency.