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Exercise

Setup up semantic masks

A common way to perform panoptic segmentation is to combine together the outputs of semantic and instance segmentation. Consider the following image of a New York street.

street image

Your task is to segment it panoptically, such that each cab is identified as a separate object, while distinguishing between the asphalt and building backgrounds.

To achieve it, you will start by producing a semantic mask with a pre-trained U-Net, available to you as UNet(). Hopefully, it should distinguish between the two background types (but not between particular cabs)!

Instructions

100 XP
  • Instantiate the U-Net model as model.
  • Generate semantic_masks by passing the input image tensor to the model.
  • Create single semantic mask by choosing the highest-probability class for each pixel.