pytorch_vision_alexnet.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	AlexNet	AlexNet competed in the 2012 ImageNet Large Scale Visual Recognition Challenge. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up.	researchers	pytorch-logo.png	Pytorch Team	CV image classification	https://github.com/pytorch/vision.git	alexnet1.png	alexnet2.png

Model Description

AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.

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

Model structure	Top-1 error	Top-5 error
alexnet	43.45	20.91

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', 'alexnet', pretrained=True)

Resources:

• One weird trick for parallelizing convolutional neural networks.