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.<\/p>\n
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.<\/p>\n
In this tutorial, you will discover how to develop a Mask R-CNN model for kangaroo object detection in photographs.<\/p>\n
After completing this tutorial, you will know:<\/p>\n
- \n
- How to prepare an object detection dataset ready for modeling with an R-CNN.<\/li>\n
- How to use transfer learning to train an object detection model on a new dataset.<\/li>\n
- How to evaluate a fit Mask R-CNN model on a test dataset and make predictions on new photos.<\/li>\n<\/ul>\n
Let\u2019s get started.<\/p>\n
![\"How](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2019\/05\/How-to-Train-an-Object-Detection-Model-to-Find-Kangaroos-in-Photographs-R-CNN-with-Keras.jpg\")
<\/p>\nHow to Train an Object Detection Model to Find Kangaroos in Photographs (R-CNN with Keras)
Photo by Ronnie Robertson<\/a>, some rights reserved.<\/p>\n<\/div>\nTutorial Overview<\/h2>\n
This tutorial is divided into five parts; they are:<\/p>\n
- \n
- How to Install Mask R-CNN for Keras<\/li>\n
- How to Prepare a Dataset for Object Detection<\/li>\n
- How to a Train Mask R-CNN Model for Kangaroo Detection<\/li>\n
- How to Evaluate a Mask R-CNN Model<\/li>\n
- How to Detect Kangaroos in New Photos<\/li>\n<\/ol>\n
How to Install Mask R-CNN for Keras<\/h2>\n
Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given image.<\/p>\n
It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are they).<\/p>\n
The Region-Based Convolutional Neural Network, or R-CNN, is a family of convolutional neural network models designed for object detection, developed by Ross Girshick<\/a>, et al. There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN. The Mask R-CNN introduced in the 2018 paper titled \u201cMask R-CNN<\/a>\u201d is the most recent variation of the family of models and supports both object detection and object segmentation. Object segmentation not only involves localizing objects in the image but also specifies a mask for the image, indicating exactly which pixels in the image belong to the object.<\/p>\n
Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model. Instead of developing an implementation of the R-CNN or Mask R-CNN model from scratch, we can use a reliable third-party implementation built on top of the Keras deep learning framework.<\/p>\n
The best-of-breed third-party implementations of Mask R-CNN is the Mask R-CNN Project<\/a> developed by Matterport<\/a>. The project is open source released under a permissive license (e.g. MIT license) and the code has been widely used on a variety of projects and Kaggle competitions.<\/p>\n
The first step is to install the library.<\/p>\n
At the time of writing, there is no distributed version of the library, so we have to install it manually. The good news is that this is very easy.<\/p>\n
Installation involves cloning the GitHub repository and running the installation script on your workstation. If you are having trouble, see the installation instructions<\/a> buried in the library\u2019s readme file.<\/p>\n
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