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.
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Constructs a ShuffleNetV2 with 0.5x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. |
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Constructs a ShuffleNetV2 with 1.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. |
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Constructs a ShuffleNetV2 with 1.5x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. |
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Constructs a ShuffleNetV2 with 2.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. |
