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Prior work has focused on binary segmentation, where the objective is to\nlabel every pixel in an image as tool or background. We improve upon previous\nwork in two major ways. First, we leverage recent techniques such as deep\nresidual learning and dilated convolutions to advance binary-segmentation\nperformance. Second, we extend the approach to multi-class segmentation, which\nlets us segment different parts of the tool, in addition to background. 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