mobilenet_v3_large¶

torchvision.models.quantization.
mobilenet_v3_large
(*, weights: Optional[Union[torchvision.models.quantization.mobilenetv3.MobileNet_V3_Large_QuantizedWeights, torchvision.models.mobilenetv3.MobileNet_V3_Large_Weights]] = None, progress: bool = True, quantize: bool = False, **kwargs: Any) → torchvision.models.quantization.mobilenetv3.QuantizableMobileNetV3[source]¶ MobileNetV3 (Large) model from Searching for MobileNetV3.
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 (
MobileNet_V3_Large_QuantizedWeights
orMobileNet_V3_Large_Weights
, optional) – The pretrained weights for the model. SeeMobileNet_V3_Large_QuantizedWeights
below for more details, and possible values. By default, no pretrained weights are used.progress (bool) – If True, displays a progress bar of the download to stderr. Default is True.
quantize (bool) – If True, return a quantized version of the model. Default is False.
**kwargs – parameters passed to the
torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights
base class. Please refer to the source code for more details about this class.

class
torchvision.models.quantization.
MobileNet_V3_Large_QuantizedWeights
(value)[source]¶ The model builder above accepts the following values as the
weights
parameter.MobileNet_V3_Large_QuantizedWeights.DEFAULT
is equivalent toMobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_QNNPACK_V1'
.MobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_V1:
These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. Also available as
MobileNet_V3_Large_QuantizedWeights.DEFAULT
.acc@1 (on ImageNet1K)
73.004
acc@5 (on ImageNet1K)
90.858
num_params
5483032
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
backend
qnnpack
recipe
unquantized
MobileNet_V3_Large_Weights.IMAGENET1K_V1
The inference transforms are available at
MobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_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=[256]
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.
MobileNet_V3_Large_Weights
(value)[source] The model builder above accepts the following values as the
weights
parameter.MobileNet_V3_Large_Weights.DEFAULT
is equivalent toMobileNet_V3_Large_Weights.IMAGENET1K_V2
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_V1'
.MobileNet_V3_Large_Weights.IMAGENET1K_V1:
These weights were trained from scratch by using a simple training recipe.
acc@1 (on ImageNet1K)
74.042
acc@5 (on ImageNet1K)
91.34
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
num_params
5483032
recipe
The inference transforms are available at
MobileNet_V3_Large_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=[256]
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]
.MobileNet_V3_Large_Weights.IMAGENET1K_V2:
These weights improve marginally upon the results of the original paper by using a modified version of TorchVision’s new training recipe. Also available as
MobileNet_V3_Large_Weights.DEFAULT
.acc@1 (on ImageNet1K)
75.274
acc@5 (on ImageNet1K)
92.566
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
num_params
5483032
recipe
The inference transforms are available at
MobileNet_V3_Large_Weights.IMAGENET1K_V2.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]
.