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

```
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, ...)
```

- 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.

`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.

Other models:
`model_alexnet()`

,
`model_mobilenet_v2()`