TinyML by Pete Warden

Author:Pete Warden
Language: eng
Format: epub
Publisher: O'Reilly Media
Published: 2019-06-18T16:00:00+00:00

Building the Dataset

To train our person detection model, we need a large collection of images that are labeled depending on whether they have people in them. The ImageNet 1,000-class dataset that’s widely used for training image classifiers doesn’t include labels for people, but luckily the COCO dataset does.

The dataset is designed to be used for training models for localization, so the images aren’t labeled with the “person,” “not person” categories for which we want to train. Instead, each image comes with a list of bounding boxes for all of the objects it contains. “Person” is one of these object categories, so to get to the classification labels we want, we need to look for images with bounding boxes for people. To make sure that they aren’t too tiny to be recognizable we also need to exclude very small bounding boxes. Slim contains a convenient script to both download the data and convert bounding boxes into labels:

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