shufflenet_v2_x2_0¶

torchvision.models.quantization.
shufflenet_v2_x2_0
(*, weights: Optional[Union[torchvision.models.quantization.shufflenetv2.ShuffleNet_V2_X2_0_QuantizedWeights, torchvision.models.shufflenetv2.ShuffleNet_V2_X2_0_Weights]] = None, progress: bool = True, quantize: bool = False, **kwargs: Any) → torchvision.models.quantization.shufflenetv2.QuantizableShuffleNetV2[source]¶ Constructs a ShuffleNetV2 with 2.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
Note
Note that
quantize = True
returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Parameters
weights (
ShuffleNet_V2_X2_0_QuantizedWeights
orShuffleNet_V2_X2_0_Weights
, optional) – The pretrained weights for the model. SeeShuffleNet_V2_X2_0_QuantizedWeights
below for more details, and possible values. By default, no pretrained weights are used.progress (bool, optional) – If True, displays a progress bar of the download to stderr. Default is True.
quantize (bool, optional) – If True, return a quantized version of the model. Default is False.
**kwargs – parameters passed to the
torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights
base class. Please refer to the source code for more details about this class.

class
torchvision.models.quantization.
ShuffleNet_V2_X2_0_QuantizedWeights
(value)[source]¶ The model builder above accepts the following values as the
weights
parameter.ShuffleNet_V2_X2_0_QuantizedWeights.DEFAULT
is equivalent toShuffleNet_V2_X2_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_FBGEMM_V1'
.ShuffleNet_V2_X2_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1:
These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below. Also available as
ShuffleNet_V2_X2_0_QuantizedWeights.DEFAULT
.acc@1 (on ImageNet1K)
75.354
acc@5 (on ImageNet1K)
92.488
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
backend
fbgemm
recipe
num_params
7393996
unquantized
ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1
The inference transforms are available at
ShuffleNet_V2_X2_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1.transforms
and perform the following preprocessing operations: AcceptsPIL.Image
, batched(B, C, H, W)
and single(C, H, W)
imagetorch.Tensor
objects. The images are resized toresize_size=[232]
usinginterpolation=InterpolationMode.BILINEAR
, followed by a central crop ofcrop_size=[224]
. Finally the values are first rescaled to[0.0, 1.0]
and then normalized usingmean=[0.485, 0.456, 0.406]
andstd=[0.229, 0.224, 0.225]
.

class
torchvision.models.
ShuffleNet_V2_X2_0_Weights
(value)[source] The model builder above accepts the following values as the
weights
parameter.ShuffleNet_V2_X2_0_Weights.DEFAULT
is equivalent toShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_V1'
.ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1:
These weights were trained from scratch by using TorchVision’s new training recipe. Also available as
ShuffleNet_V2_X2_0_Weights.DEFAULT
.acc@1 (on ImageNet1K)
76.23
acc@5 (on ImageNet1K)
93.006
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
recipe
num_params
7393996
The inference transforms are available at
ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1.transforms
and perform the following preprocessing operations: AcceptsPIL.Image
, batched(B, C, H, W)
and single(C, H, W)
imagetorch.Tensor
objects. The images are resized toresize_size=[232]
usinginterpolation=InterpolationMode.BILINEAR
, followed by a central crop ofcrop_size=[224]
. Finally the values are first rescaled to[0.0, 1.0]
and then normalized usingmean=[0.485, 0.456, 0.406]
andstd=[0.229, 0.224, 0.225]
.