resnet50¶
- torchvision.models.quantization.resnet50(*, weights: Optional[Union[ResNet50_QuantizedWeights, ResNet50_Weights]] = None, progress: bool = True, quantize: bool = False, **kwargs: Any) QuantizableResNet [source]¶
ResNet-50 model from Deep Residual Learning for Image Recognition
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 (
ResNet50_QuantizedWeights
orResNet50_Weights
, optional) – The pretrained weights for the model. SeeResNet50_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.QuantizableResNet
base class. Please refer to the source code for more details about this class.
- class torchvision.models.quantization.ResNet50_QuantizedWeights(value)[source]¶
The model builder above accepts the following values as the
weights
parameter.ResNet50_QuantizedWeights.DEFAULT
is equivalent toResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V2
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_FBGEMM_V1'
.ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1:
These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below.
acc@1 (on ImageNet-1K)
75.92
acc@5 (on ImageNet-1K)
92.814
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
backend
fbgemm
recipe
num_params
25557032
unquantized
ResNet50_Weights.IMAGENET1K_V1
GIPS
4.09
File size
24.8 MB
The inference transforms are available at
ResNet50_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=[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]
.ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V2:
These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized weights listed below. Also available as
ResNet50_QuantizedWeights.DEFAULT
.acc@1 (on ImageNet-1K)
80.282
acc@5 (on ImageNet-1K)
94.976
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
backend
fbgemm
recipe
num_params
25557032
unquantized
ResNet50_Weights.IMAGENET1K_V2
GIPS
4.09
File size
25.0 MB
The inference transforms are available at
ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_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]
.
- class torchvision.models.ResNet50_Weights(value)[source]
The model builder above accepts the following values as the
weights
parameter.ResNet50_Weights.DEFAULT
is equivalent toResNet50_Weights.IMAGENET1K_V2
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_V1'
.ResNet50_Weights.IMAGENET1K_V1:
These weights reproduce closely the results of the paper using a simple training recipe.
acc@1 (on ImageNet-1K)
76.13
acc@5 (on ImageNet-1K)
92.862
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
num_params
25557032
recipe
GFLOPS
4.09
File size
97.8 MB
The inference transforms are available at
ResNet50_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]
.ResNet50_Weights.IMAGENET1K_V2:
These weights improve upon the results of the original paper by using TorchVision’s new training recipe. Also available as
ResNet50_Weights.DEFAULT
.acc@1 (on ImageNet-1K)
80.858
acc@5 (on ImageNet-1K)
95.434
min_size
height=1, width=1
categories
tench, goldfish, great white shark, … (997 omitted)
num_params
25557032
recipe
GFLOPS
4.09
File size
97.8 MB
The inference transforms are available at
ResNet50_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]
.