Welcome to part 3 of the TensorFlow Object Detection API tutorial series. In this part and the subsequent few, we're going to cover how we can track and detect our own custom objects with this API.
Going from using the pre-built models to adding custom objects is a decent jump from my findings, and I could not locate any full step-by-step guides, so hopefully I can save you all from the struggle. Once solved, the ability to train for any custom object you can think of (and create data for) is an awesome skill to have.
Text tutorials and sample code: https://pythonprogramming.net/custom-objects-tracking-tensorflow-object-detection-api-tutorial/
Paperspace $10 referral link: https://goo.gl/h7SSkv
Paperspace overview and comparison to AWS and Azure: https://www.youtube.com/watch?v=8RJX8WNcXyI
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