Budget Automation for Bounding Box Annotation

Learn how to use TensorFlow.js to speed up data annotation. In this blog post, I will show you the automation technique I used to quickly prototype a 3D print failure detection model.

Data collection and preparation are the foundation of every machine learning application. You’ve heard it before: “Garbage in, garbage out” in reference to an algorithm’s limited capability to correct for inaccurate, poor-quality, or biased input data. The cost of quality annotated data prompted a cottage industry of tools/platforms for speeding up the data labeling process. Besides the SaaS/on-prem startup ecosystem, each of the major cloud providers (AWS, Microsoft, Google) launched an automated data labeling product in the last two years. Understandably, these services are often developed with Premium/Enterprise users, features, and price points in mind.

On a limited budget, am I stuck labeling everything by hand?

Good, news! With a bit of elbow grease, you can automate bounding box annotation for yourself or a small team. In this blog post, I will show you the automation technique I used to quickly prototype a 3D print failure detection model.

You’ll learn how to:

1. Create detailed instructions for human labelers
2. Train a guidance model
3. Automate bounding box annotation with Microsoft VoTT (Visual Object Tagging Tool) and TensorFlow.js

tensorflowjs object-detection tensorflow data-annotation-tools automl

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