pytorch_vision_densenet.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	Densenet	Dense Convolutional Network (DenseNet), connects each layer to every other layer in a feed-forward fashion.	researchers	pytorch-logo.png	Pytorch Team	CV image classification	https://github.com/pytorch/vision.git	densenet1.png	densenet2.png

Model Description

Dense Convolutional Network (DenseNet), connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.

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

Model structure	Top-1 error	Top-5 error
densenet121	25.35	7.83
densenet169	24.00	7.00
densenet201	22.80	6.43
densenet161	22.35	6.20

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', 'densenet121', pretrained=True)
model = torch.hub.load('pytorch/vision', 'densenet169', pretrained=True)
model = torch.hub.load('pytorch/vision', 'densenet201', pretrained=True)
model = torch.hub.load('pytorch/vision', 'densenet161', pretrained=True)

Resources:

• Densely Connected Convolutional Networks.