resnet50¶
- torchvision.models.resnet50(*, weights: Optional[ResNet50_Weights] = None, progress: bool = True, **kwargs: Any) ResNet [source]¶
ResNet-50 from Deep Residual Learning for Image Recognition.
Note
The bottleneck of TorchVision places the stride for downsampling to the second 3x3 convolution while the original paper places it to the first 1x1 convolution. This variant improves the accuracy and is known as ResNet V1.5.
- Parameters:
weights (
ResNet50_Weights
, optional) – The pretrained weights to use. SeeResNet50_Weights
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.
**kwargs – parameters passed to the
torchvision.models.resnet.ResNet
base class. Please refer to the source code for more details about this class.
- 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
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
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]
.