How to Train an Object Detection Model with Keras

Last Updated on September 2, 2020

Object detection is a challenging computer vision task that involves predicting both where the objects are in the image and what type of objects were detected.

The Mask Region-based Convolutional Neural Network, or Mask R-CNN, model is one of the state-of-the-art approaches for object recognition tasks. The Matterport Mask R-CNN project provides a library that allows you to develop and train Mask R-CNN Keras models for your own object detection tasks. Using the library can be tricky for beginners and requires the careful preparation of the dataset, although it allows fast training via transfer learning with top performing models trained on challenging object detection tasks, such as MS COCO.

In this tutorial, you will discover how to develop a Mask R-CNN model for kangaroo object detection in photographs.

After completing this tutorial, you will know:

How to prepare an object detection dataset ready for modeling with an R-CNN.
How to use transfer learning to train an object detection model on a new dataset.
How to evaluate a fit Mask R-CNN model on a test dataset and make predictions on new photos.

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