Homework: Collect an Image Dataset

Background

It helps to learn how to see the world through the eyes of a machine when designing for machine learning and more for computer vision. Now that you have a basic understanding of what it takes to train an image recognition model, it is time to go back into the field to collect a more targeted dataset that you can use to prototype with and evaluate the feasibility of your product idea.

Learning Objectives

After completing this exercise, you can collect a small-scale street-level image dataset and label it for model training.

Instructions

  • Go outside and, with a camera or your smartphone, take photos of the various classes your envisioned product’s model should be able to recognize
  • Collect ample images for each label – ideally, as a group, you end up with around 25-50 images per label, but more may be better.
  • Also, collect an equal amount of images for the “null” class (one that does not show the things you are interested in)
    • Make sure these images are as varied as possible so that the classifier will be very accurate at recognizing this label (low false negatives / high true positives)
  • Download the images to your local computer.
  • Label the images in the training set by putting each in a named folder for that label (i.e., class)
  • Upload the dataset to a cloud service for easy use during follow-up exercises.

Product

After completing this exercise, you will have a dataset that you can use to train a model in Google Teachable Machine that approximates the model your envisioned responsible urban AI system will operate on.

Follow-up

In next week’s session, you will pool your dataset with those of your group members and use it to train and test a model.