Njobvu-AI: An open-source tool for collaborative image labeling and
  implementation of computer vision models
Jonathan S. Koning Ashwin Subramanian Mazen Alotaibi Cara L. Appel Christopher M. Sullivan Thon Chao Lisa Truong Robyn L. Tanguay Pankaj Jaiswal Taal Levi Damon B. Lesmeister
Practitioners interested in using computer vision models lack user-friendly
and open-source software that combines features to label training data, allow
multiple users, train new algorithms, review output, and implement new models.
Labeling training data, such as images, is a key step to developing accurate
object detection algorithms using computer vision. This step is often not
compatible with many cloud-based services for marking or labeling image and
video data due to limited internet bandwidth in many regions of the world.
Desktop tools are useful for groups working in remote locations, but users
often do not have the capability to combine projects developed locally by
multiple collaborators. Furthermore, many tools offer features for labeling
data or using pre-trained models for classification, but few allow researchers
to combine these steps to create and apply custom models. Free, open-source,
and user-friendly software that offers a full suite of features (e.g., ability
to work locally and online, and train custom models) is desirable to field
researchers and conservationists that may have limited coding skills. We
developed Njobvu-AI, a free, open-source tool that can be run on both desktop
and server hardware using Node.js, allowing users to label data, combine
projects for collaboration and review, train custom algorithms, and implement
new computer vision models. The name Njobvu-AI (pronounced N-joh-voo AI),
incorporating the Chichewa word for elephant, is inspired by a wildlife
monitoring program in Malawi that was a primary impetus for the development of
this tool and references similarities between the powerful memory of elephants
and properties of computer vision models.