PeopleSegNet ============ The model described in this card detects one or more “person” objects within an image and returns a box around each object, as well as a segmentation mask for each object. This model is based on MaskRCNN with ResNet50 as its feature extractor. :ref:`MaskRCNN` is a widely adopted two-stage architecture, which uses Region Proposal Network (RPN) to generate object proposals and various prediction heads to predict object categories, refine bounding boxes and generate instance masks. Training Algorithm ------------------ The training algorithm optimizes the network to minimize the mask, localization and confidence loss for the objects. Intended Use ------------ Primary use case intended for the model is detecting and segmenting people in a color (RGB) image. The model can be used to detect and segment people from photos and videos by using appropriate video or image decoding and pre-processing. The datasheet for the model is captured in it's model card hosted at `NGC`_. .. _NGC: https://ngc.nvidia.com/catalog/models/nvidia:tlt_peoplesegnet