model_resnet: ResNet implementation

In torchvision: Models, Datasets and Transformations for Images

ResNet implementation

Description

ResNet models implementation from Deep Residual Learning for Image Recognition and later related papers (see Functions)

Usage

model_resnet18(pretrained = FALSE, progress = TRUE, ...)

model_resnet34(pretrained = FALSE, progress = TRUE, ...)

model_resnet50(pretrained = FALSE, progress = TRUE, ...)

model_resnet101(pretrained = FALSE, progress = TRUE, ...)

model_resnet152(pretrained = FALSE, progress = TRUE, ...)

model_resnext50_32x4d(pretrained = FALSE, progress = TRUE, ...)

model_resnext101_32x8d(pretrained = FALSE, progress = TRUE, ...)

model_wide_resnet50_2(pretrained = FALSE, progress = TRUE, ...)

model_wide_resnet101_2(pretrained = FALSE, progress = TRUE, ...)

Arguments

pretrained	(bool): If TRUE, returns a model pre-trained on ImageNet.
progress	(bool): If TRUE, displays a progress bar of the download to stderr.
...	Other parameters passed to the resnet model.

Functions

• model_resnet18(): ResNet 18-layer model

• model_resnet34(): ResNet 34-layer model

• model_resnet50(): ResNet 50-layer model

• model_resnet101(): ResNet 101-layer model

• model_resnet152(): ResNet 152-layer model

• model_resnext50_32x4d(): ResNeXt-50 32x4d model from "Aggregated Residual Transformation for Deep Neural Networks" with 32 groups having each a width of 4.

• model_resnext101_32x8d(): ResNeXt-101 32x8d model from "Aggregated Residual Transformation for Deep Neural Networks" with 32 groups having each a width of 8.

• model_wide_resnet50_2(): Wide ResNet-50-2 model from "Wide Residual Networks" with width per group of 128.

• model_wide_resnet101_2(): Wide ResNet-101-2 model from "Wide Residual Networks" with width per group of 128.

See Also

Other classification_model: model_alexnet(), model_convnext, model_efficientnet, model_efficientnet_v2, model_facenet, model_inception_v3(), model_maxvit(), model_mobilenet_v2(), model_mobilenet_v3, model_vgg, model_vit

torchvision documentation built on Nov. 6, 2025, 9:07 a.m.