Simple Mask R-CNN Implementation

This project is a little experimental implementation of the Mask R-CNN algorithm for image segmentation. As for the Experiment, I wanted to find out whether the following priciple actually holds:

Given a backbone that performs well on classifying my objects
(on single-object images), that backbone is also suitable for
detecting and masking my objects (on multi-object images).

My limited resources inspired me to try this principle with a "minimal" example, i.e. the MNIST dataset. Therefore, the goal of this project is detection and masking of scattered MNIST-letters.

Basis

• Most of the model implementation is inspired by the keras implementation by Matterport.
• The MNIST backbone was developed based on the keras MNIST example.

Functionality

Data Generation

• Crop and mask images: crop_and_mask_image_files()
• Load data like the mnist-module: load_data()
• Generate random images with labels: generate_labeled_data_files()
• Load generated data: load_labeled_data()

Data Parsing/Inspection

• Parse data: load_backbone_pretraining_data(), load_maskrcnn_data()
• Inspect data: write_solutions()