This is a follow-up post on “Building a Real-Time Object Recognition App with Tensorflow and OpenCV” where I focus on training my own classes. Specifically, I trained my own Raccoon detector on a dataset that I collected and labeled by myself. The full dataset is available on my Github repo.
By the way, here is the Raccoon detector in action:
After my last post, a lot of people asked me to write a guide on how they can use TensorFlow’s new Object Detector API to train an object detector with their own dataset. I found some time to do it. In this post, I will explain all the necessary steps to train your own detector. In particular, I created an object detector that is able to recognize Racoons with relatively good results.
Nothing special? they are one of my favorite animals and somehow they are also my neighbors! I swear, there are so many potential use cases with the Raccoon detector. For example, now you can detect if a Raccoon is knocking on your door while you’re not at home. The system could send you a push message to your mobile phone so that you know that you have some visitors.