Open Images Pre-trained Semantic Segmentation
=============================================

Semantic segmentation assigns every pixel in an image to a class label. Semantic segmentation does
image classification at pixel level. Unlike instance segmentation which can label individual
instances belonging to a class, semantic segmentation clubs all instances of a class to same label.
This model object contains pretrained weights that may be used as a starting point with the following
semantic segmentation networks in Transfer Learning Toolkit (TLT) to facilitate transfer learning.

The following semantic segmentation architecture are supported:

* UNet

Supported Backbones
-------------------

The pre-trained weights are trained on a subset of the Google OpenImages dataset. Following backbones are supported with these UNet networks.

* resnet10/resnet18/resnet34/resnet50/resnet101
* vgg16/vgg19

.. Note::
     - These are unpruned models with just the feature extractor weights, and may not be used without retraining
       to deploy in a classification application.
     - Please make sure to set the :code:`all_projections` field to :code:`False` in the spec file when training a ResNet101 model.

For more instructions on downloading and using the models defined here, refer to the `NGC catalog page`_.

.. _NGC catalog page: https://ngc.nvidia.com/catalog/models/nvidia:tlt_semantic_segmentation