pytorch_vision_squeezenet.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	Squeezenet	Alexnet-level accuracy with 50x fewer parameters.	researchers	pytorch-logo.png	Pytorch Team	CV image classification	https://github.com/pytorch/vision.git	squeezenet.png	no-image

Model Description

Model squeezenet1_0 is from the SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size paper

Model squeezenet1_1 is from the official squeezenet repo. It has 2.4x less computation and slightly fewer parameters than squeezenet1_0, without sacrificing accuracy.

Their 1-crop error rates on imagenet dataset with pretrained models are listed below.

Model structure	Top-1 error	Top-5 error
squeezenet1_0	41.90	19.58
squeezenet1_1	41.81	19.38

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', 'squeezenet1_0', pretrained=True)
model = torch.hub.load('pytorch/vision', 'squeezenet1_1', pretrained=True)

Resources:

• Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5MB model size.