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Deep Mask employs a fairly traditional feedforward deep network design.In such networks, with progressively deeper network stages information is more abstract and semantically meaningful.

Together, they have enabled FAIR’s machine vision systems to detect and precisely delineate every object in an image. We’re making the code for Deep Mask Sharp Mask as well as Multi Path Net — along with our research papers and demos related to them — open and accessible to all, with the hope that they’ll help rapidly advance the field of machine vision. And you probably notice countless other details as well.

Here we are following the foundational paradigm called Region-CNN, or RCNN for short, pioneered by Ross Girshick (now also a member of FAIR).

RCNN is a two-stage procedure where a first stage is used to draw attention to certain image regions, and in a second stage a deep net is used to identify the objects present.

The final stage of our recognition pipeline uses a specialized convolutional net, which we call Multi Path Net, to label each object mask with the object type it contains (e.g. As we continue improving these core technologies we’ll continue publishing our latest results and updating the open source tools we make available to the community. A machine sees none of this; an image is encoded as an array of numbers representing color values for each pixel, as in the second photo, the one on the right.

Let’s take a look at the building blocks of these algorithms. So how do we enable machine vision to go from pixels to a deeper understanding of an image?

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At Facebook AI Research (FAIR) we’re pushing machine vision to the next stage — our goal is to similarly understand images and objects at the pixel level.

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