Building A Mask R-CNN From Scratch in TensorFlow and Keras

In this post, we'll explanation of how to build a basic Mask R-CNN for learning purposes, without the hustle and bustle.

If you ever wanted to implement a Mask R-CNN from scratch in TensorFlow, you probably found Matterport’s implementation¹. This is a great one, if you only want to _use _a Mask R-CNN. However, as it is very robust and complex, it can be hard to thoroughly understand every bit of it. And the even bigger problem is, that it doesn’t run with new versions of TensorFlow.

And if you would have given a chance to a PyTorch implementation, the most frequently used one is the Detectron2², which is also very hard to understand because of its complexity.

From all the descriptions of how Mask R-CNN works, it always seems very easy to implement it, but somehow you still can’t find a lot of implementations.

object-detection tensorflow keras mask-r-cnn machine-learning

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This tutorial shows how to train Mask R-CNN on a custom dataset using TensorFlow 1.14 and Keras. Mask R-CNN is an object detection model based on deep convolutional neural networks (CNN) developed by a group of Facebook AI researchers in 2017. The model can return both the bounding box and a mask for each detected object in an image.

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Let’s understand these state-of-the-art region proposal based convolution neural networks in detail. I’ve discussed Object Detection and R-CNN in detail in my previous article.