ResNeXt

The ResNeXt model is based on the “Aggregated Residual Transformations for Deep Neural Networks” paper.

Architecture overview

This paper improves the ResNet architecture by increasing the width of bottleneck blocks

The key takeaways from the paper are the following:

• increases the number of channels in bottlenecks

• switches to group convolutions to balance the number of operations

Model builders

The following model builders can be used to instantiate a ResNet model, with or without pre-trained weights. All the model builders internally rely on the holocron.models.classification.resnet.ResNet base class. Please refer to the source code for more details about this class.

resnext50_32x4d([pretrained, checkpoint, ...])	ResNeXt-50 from "Aggregated Residual Transformations for Deep Neural Networks"
resnext101_32x8d([pretrained, checkpoint, ...])	ResNeXt-101 from "Aggregated Residual Transformations for Deep Neural Networks"