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.
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ResNeXt-50 from "Aggregated Residual Transformations for Deep Neural Networks" |
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ResNeXt-101 from "Aggregated Residual Transformations for Deep Neural Networks" |