pytorch_vision_googlenet.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	GoogLeNet	GoogLeNet was based on a deep convolutional neural network architecture codenamed "Inception" which won ImageNet 2014.	researchers	pytorch-logo.png	Pytorch Team	CV image classification	https://github.com/pytorch/vision.git	googlenet1.png	googlenet2.png

Model Description

GoogLeNet was based on a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). The 1-crop error rates on the ImageNet dataset with a pretrained model are list below.

Model structure	Top-1 error	Top-5 error
googlenet	30.22	10.47

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', 'googlenet', pretrained=True)

Resources:

• Going Deeper with Convolutions