DeepMask

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Implementations

DeepMask is trained with two objectives: given an image patch, one branch of the model outputs a class-agnostic segmentation mask, while the other branch outputs how likely the patch is to contain an object. At test time, DeepMask is applied densely to an image and generates a set of object masks, each with a corresponding objectness score. These masks densely cover the objects in an image and can be used as a first step for object detection and other tasks in computer vision.

DeepMask

SharpMask

Github: https://github.com/facebookresearch/deepmask

i-segmentationdeepmask
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