Machine Learning for Social Good
2019-07-24, 17:25–17:35, Room 349

Using Julia and Flux.jl, we want to show how we have applied modern neural architectures like Mask RCNN and Inception to identify diseases and slums in metropolitan cities.

ML has provided us with the tools to think of data as a source of insight. It isn’t a stretch to apply the same thinking towards some of the most pressing socio economic calamities we as a society are faced with.

Mask RCNN is a state of the art object detection and segmentation net that was developed by FAIR and has shown tremendous leaps in terms of segmentation while being conceptually simple. We present an all-Julia Flux implementation of Mask RCNN and our methodology of setting up the segmentation task. We use satellite images of cities and try to identify regions where slums exist.

Finally we share our results of our findings.

The code is currently private before it is released openly through contributions to various existing projects like the Flux model-zoo and Metalhead.jl.