The sublocks of the resnet architecture can be defined as BasicBlock or Bottleneck based on the used resnet depth. E.g. resnet18 and resnet32 32 use BasicBlock, while resnet>=50 27 use Bottleneck.


Yes. Your mentioned configuration would fit resnet34 and resnet50 as seen here 56.


Bottleneck layers support the groups argument to create grouped convolutions. (line of code 101)


Again, a ResNeXt-specific setup for the Bottleneck layer. You could try different values, but would most likely have to look into the paper, how these values would interact with the channels etc.


You could treat your binary classification use case as a 2 class multi-class classification use case, if you set the number of output features to 2.