In a data science or machine learning project, you may prepare and study images or other data within a Jupyter notebook then need to annotate the data to augment the training or fix errors in your source data. You may need to check classifications are correct (cats/dogs correctly specified) or add bounding boxes around the pertinent parts of your images. For example, to build a dog breed classified you might first build a model that learns how to identify the bounding box of a dog within the image, then your final model zooms in on that box in order to train/evaluate the breed classifier.

Since you are already working within a Jupyter notebook, the Innotater works inline allowing you to interact with your data and annotate it quickly and easily, syncing straight back to your input data arrays or matrices.

Within Jupyter, you can easily home in on problem input data - perhaps only misclassified images - so you can step through and adjust bounding boxes just for those items.

The Innotater widget is designed with a flexible API making it quick and easy to get started exploring your data, guessing how to work with your data without explicit configuration where possible.

The widget is currently in ALPHA development phase, and I appreciate all feedback on any problems including details on how the current code works or fails to work for the structure of your particular projects.

Screenshot of Innotater widget in Jupyter

Screenshot of Innotater widget in Jupyter