ShuffleNet V2

The ShuffleNet V2 model is based on the ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design paper.

Model builders

The following model builders can be used to instantiate a ShuffleNetV2 model, with or without pre-trained weights. All the model builders internally rely on the torchvision.models.shufflenetv2.ShuffleNetV2 base class. Please refer to the source code for more details about this class.

shufflenet_v2_x0_5(*[, weights, progress])	Constructs a ShuffleNetV2 architecture with 0.5x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
shufflenet_v2_x1_0(*[, weights, progress])	Constructs a ShuffleNetV2 architecture with 1.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
shufflenet_v2_x1_5(*[, weights, progress])	Constructs a ShuffleNetV2 architecture with 1.5x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
shufflenet_v2_x2_0(*[, weights, progress])	Constructs a ShuffleNetV2 architecture with 2.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.