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Quantized ShuffleNet V2

The Quantized 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 quantized ShuffleNetV2 model, with or without pre-trained weights. All the model builders internally rely on the torchvision.models.quantization.shufflenetv2.QuantizableShuffleNetV2 base class. Please refer to the source code for more details about this class.

shufflenet_v2_x0_5(*[, weights, progress, …])

Constructs a ShuffleNetV2 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 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 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 with 2.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.

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