The purpose of this post is to introduce the “micro-model” methodology we use at Cord to automate data annotation. We have deployed this approach

Introduction to micro-models or: how I learned to stop worrying and love overfitting

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2021-08-05 15:00:08

The purpose of this post is to introduce the “micro-model” methodology we use at Cord to automate data annotation. We have deployed this approach on computer vision labelling tasks across a wide range of domains including medical imaging, agriculture, autonomous vehicles, and satellite imaging.

TLDR; What: Low bias models applied to a small domain of a data distribution. How: Overfitting deep learning models on a handful of examples of a narrowly defined task. Why: Saving hundreds of hours of hand labelling.

This of course depends on your goal. Maybe you want a general purpose model that can detect the Batmen of Adam West, Michael Keaton, and Batfleck all in one. Maybe you need it to include a Bruce Wayne detector that can also identify the man behind the mask.

But if you want a model that follows the Christian Bale Batman in one movie, in one scene, the answer is…five labelled images. The model used to produce the snippet of model inference results above was trained with the five labels below:

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