pytorch_vision_inception_v3.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	Inception_v3	1st Runner Up for image classification in ILSVRC (ImageNet Large Scale Visual Recognition Competition) 2015.	researchers	pytorch-logo.png	Pytorch Team	CV image classification	https://github.com/pytorch/vision.git	inception_v3.png	no-image

Model Description

Inception v3: Based on the exploration of ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error on the validation set (3.6% error on the test set) and 17.3% top-1 error on the validation set.

The 1-crop error rates on the imagenet dataset with the pretrained model are listed below.

Model structure	Top-1 error	Top-5 error
inception_v3	22.55	6.44

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', 'inception_v3', pretrained=True)

Resources:

• Rethinking the Inception Architecture for Computer Vision.