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
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
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
all_projectionsfield toFalsein 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.