pytorch_vision_resnet.md

layout	background-class	body-class	title	summary	category	image	author	tags	github-link	featured_image_1	featured_image_2
pytorch_hub_detail	pytorch-hub-background	pytorch-hub	Resnet	Deep residual networks that is pre-trained from ImageNet database.	researchers	pytorch-logo.png	Pytorch Team	CV image classification	https://github.com/pytorch/vision.git	resnet.png	no-image

Model Description

Resnet models were proposed in "Deep Residual Learning for Image Recognition". Here we have the 5 versions of resnet models, which contains 5, 34, 50, 101, 152 layers respectively. Detailed model architectures can be found in Table 1. Their 1-crop error rates on imagenet dataset with pretrained models are list below.

Model structure	Top-1 error	Top-5 error
resnet18	30.24	10.92
resnet34	26.70	8.58
resnet50	23.85	7.13
resnet101	22.63	6.44
resnet152	21.69	5.94

Notes on Inputs

All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. You can use the following transform to normalize:

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])

Example:

import torch
model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)
model = torch.hub.load('pytorch/vision', 'resnet34', pretrained=True)
model = torch.hub.load('pytorch/vision', 'resnet50', pretrained=True)
model = torch.hub.load('pytorch/vision', 'resnet101', pretrained=True)
model = torch.hub.load('pytorch/vision', 'resnet152', pretrained=True)

Resources:

• Deep Residual Learning for Image Recognition