Implementation of ResNeXt models from the paper Aggregated Residual Transformations for Deep Neural Networks in Keras 2.0+.
Contains code for building the general ResNeXt model (optimized for datasets similar to CIFAR) and ResNeXtImageNet (optimized for the ImageNet dataset).
ResNeXt updates the ResNet block with a new expanded block architecture, which depends on the
cardinality parameter. It can be further visualised in the below diagram from the paper.
However, since grouped convolutions are not directly available in Keras, an equivalent variant is used in this repository (see block 2)
For the general ResNeXt model (for all datasets other than ImageNet),
from resnext import ResNeXt model = ResNeXt(image_shape, depth, cardinality, width, weight_decay)
For the ResNeXt model which has been optimized for ImageNet,
from resnext import ResNeXtImageNet image_shape = (112, 112, 3) if K.image_data_format() == 'channels_last' else (3, 112, 112) model = ResNeXtImageNet(image_shape)
Note, there are other parameters such as depth, cardinality, width and weight_decay just as in the general model, however the defaults are set according to the paper.