shufflenet_v2_x1_0

torchvision.models.quantization.shufflenet_v2_x1_0(*, weights: Optional[Union[torchvision.models.quantization.shufflenetv2.ShuffleNet_V2_X1_0_QuantizedWeights, torchvision.models.shufflenetv2.ShuffleNet_V2_X1_0_Weights]] = None, progress: bool = True, quantize: bool = False, **kwargs: Any) → torchvision.models.quantization.shufflenetv2.QuantizableShuffleNetV2

Constructs a ShuffleNetV2 with 1.0x 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_X1_0_QuantizedWeights or ShuffleNet_V2_X1_0_Weights, optional) – The pretrained weights for the model. See ShuffleNet_V2_X1_0_QuantizedWeights below for more details, and possible values. By default, no pre-trained 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_X1_0_QuantizedWeights base class. Please refer to the source code for more details about this class.

class torchvision.models.quantization.ShuffleNet_V2_X1_0_QuantizedWeights(value)

The model builder above accepts the following values as the weights parameter. ShuffleNet_V2_X1_0_QuantizedWeights.DEFAULT is equivalent to ShuffleNet_V2_X1_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1. You can also use strings, e.g. weights='DEFAULT' or weights='IMAGENET1K_FBGEMM_V1'.

ShuffleNet_V2_X1_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_X1_0_QuantizedWeights.DEFAULT.

acc@1 (on ImageNet-1K)	68.36
acc@5 (on ImageNet-1K)	87.582
min_size	height=1, width=1
categories	tench, goldfish, great white shark, … (997 omitted)
backend	fbgemm
recipe	link
num_params	2278604
unquantized	ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1

The inference transforms are available at ShuffleNet_V2_X1_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. The images are resized to resize_size=[256] using interpolation=InterpolationMode.BILINEAR, followed by a central crop of crop_size=[224]. Finally the values are first rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].

class torchvision.models.ShuffleNet_V2_X1_0_Weights(value)

The model builder above accepts the following values as the weights parameter. ShuffleNet_V2_X1_0_Weights.DEFAULT is equivalent to ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1. You can also use strings, e.g. weights='DEFAULT' or weights='IMAGENET1K_V1'.

ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1:

These weights were trained from scratch to reproduce closely the results of the paper. Also available as ShuffleNet_V2_X1_0_Weights.DEFAULT.

acc@1 (on ImageNet-1K)	69.362
acc@5 (on ImageNet-1K)	88.316
min_size	height=1, width=1
categories	tench, goldfish, great white shark, … (997 omitted)
recipe	link
num_params	2278604

The inference transforms are available at ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. The images are resized to resize_size=[256] using interpolation=InterpolationMode.BILINEAR, followed by a central crop of crop_size=[224]. Finally the values are first rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].