Techniques for using instance segmentation with machine learning (ML) models are discussed herein. An image can be provided as input to a ML model, which can generate, as an output from the ML model, a feature map comprising a plurality of features. Each feature of the plurality of features can comprise a confidence score, classification information, and a region of interest (ROI) determined in accordance with a non-maximal suppression (NMS) technique. Individual ROIs that are similar can be associated together for segmentation purposes. That is, instead of requiring a second ML model and/or a second operation to segment the image (e.g., identify which pixels correspond with the detected object, for example, by outputting a mask or set of lines and/or curves), the techniques discussed herein substantially simultaneously detect an object (e.g., determine an ROI) and segment the image.