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MIDS Capstone Project Fall 2021

PyLabel

PyLabel is a Python package to help computer vision practitioners get labelled data sets ready to be used in deep learning models. The core features of this package are: a labelling tool (PyLabeler) that runs in a Jupyter notebook that can be used to view, edit, or add annotations to an image dataset, an AI assisted labeling to decrease the time spent manually labelling images, and the ability to convert between labelling formats including YOLO, COCO, and VOC. A beta of the package has already been released and has been installed almost 1,000 times. See https://github.com/pylabel-project/

It can be installed in Python with:

pip install pylabel

PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. It can translate bounding box annotations between different formats. (For example, COCO to YOLO.) And it includes an AI-assisted labeling tool that runs in a Jupyter notebook. Some of the features are:

Translate: Convert annotation formats with a single line of code:

Analyze: PyLabel stores annotatations in a pandas dataframe so you can easily perform analysis on image datasets.

Split: Divide image datasets into train, test, and val with stratification to get consistent class distribution.

Label: PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model.

Visualize: Render images from your dataset with bounding boxes overlaid so you can confirm the accuracy of the annotations.

 


Tutorial Notebooks

See PyLabel in action in these sample Jupyter notebooks:


About PyLabel 

PyLabel is being developed by Jeremy Fraenkel, Alex Heaton, and Derek Topper as the Capstope project for the Master of Information and Data Science (MIDS) at the UC Berkeley School of Information. If you have any questions or feedback please [create an issue](https://github.com/pylabelalpha/package/issues). Please let us know how we can make PyLabel more useful. 
 

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Last updated: December 7, 2021