Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'acb81357c4'
${ noResults }
pytorch-image-models/models/dpn/index.html

1628 lines
42 KiB
Raw Blame History

<!doctype html>
<html lang="en" class="no-js">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<meta name="description" content="Pretained Image Recognition Models">
<link rel="icon" href="../../assets/images/favicon.png">
<meta name="generator" content="mkdocs-1.1.2, mkdocs-material-7.0.6">
<title>Dual Path Network (DPN) - Pytorch Image Models</title>
<link rel="stylesheet" href="../../assets/stylesheets/main.2c0c5eaf.min.css">
<link rel="stylesheet" href="../../assets/stylesheets/palette.7fa14f5b.min.css">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,400i,700%7CRoboto+Mono&display=fallback">
<style>:root{--md-text-font-family:"Roboto";--md-code-font-family:"Roboto Mono"}</style>
</head>
<body dir="ltr" data-md-color-scheme="" data-md-color-primary="none" data-md-color-accent="none">
<input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
<input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
<label class="md-overlay" for="__drawer"></label>
<div data-md-component="skip">
<a href="#dual-path-network-dpn" class="md-skip">
Skip to content
</a>
</div>
<div data-md-component="announce">
</div>
<header class="md-header" data-md-component="header">
<nav class="md-header__inner md-grid" aria-label="Header">
<a href="../.." title="Pytorch Image Models" class="md-header__button md-logo" aria-label="Pytorch Image Models" data-md-component="logo">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54z"/></svg>
</a>
<label class="md-header__button md-icon" for="__drawer">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2z"/></svg>
</label>
<div class="md-header__title" data-md-component="header-title">
<div class="md-header__ellipsis">
<div class="md-header__topic">
<span class="md-ellipsis">
Pytorch Image Models
</span>
</div>
<div class="md-header__topic" data-md-component="header-topic">
<span class="md-ellipsis">
Dual Path Network (DPN)
</span>
</div>
</div>
</div>
<div class="md-header__options">
</div>
<label class="md-header__button md-icon" for="__search">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5z"/></svg>
</label>
<div class="md-search" data-md-component="search" role="dialog">
<label class="md-search__overlay" for="__search"></label>
<div class="md-search__inner" role="search">
<form class="md-search__form" name="search">
<input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" data-md-state="active" required>
<label class="md-search__icon md-icon" for="__search">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5z"/></svg>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12z"/></svg>
</label>
<button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41L17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41z"/></svg>
</button>
</form>
<div class="md-search__output">
<div class="md-search__scrollwrap" data-md-scrollfix>
<div class="md-search-result" data-md-component="search-result">
<div class="md-search-result__meta">
Initializing search
</div>
<ol class="md-search-result__list"></ol>
</div>
</div>
</div>
</div>
</div>
<div class="md-header__source">
<a href="https://github.com/rwightman/pytorch-image-models/" title="Go to repository" class="md-source" data-md-component="source">
<div class="md-source__icon md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M439.55 236.05L244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
</div>
<div class="md-source__repository">
rwightman/pytorch-image-models
</div>
</a>
</div>
</nav>
</header>
<div class="md-container" data-md-component="container">
<main class="md-main" data-md-component="main">
<div class="md-main__inner md-grid">
<div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
<div class="md-sidebar__scrollwrap">
<div class="md-sidebar__inner">
<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
<label class="md-nav__title" for="__drawer">
<a href="../.." title="Pytorch Image Models" class="md-nav__button md-logo" aria-label="Pytorch Image Models" data-md-component="logo">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54z"/></svg>
</a>
Pytorch Image Models
</label>
<div class="md-nav__source">
<a href="https://github.com/rwightman/pytorch-image-models/" title="Go to repository" class="md-source" data-md-component="source">
<div class="md-source__icon md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M439.55 236.05L244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
</div>
<div class="md-source__repository">
rwightman/pytorch-image-models
</div>
</a>
</div>
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="../.." class="md-nav__link">
Getting Started
</a>
</li>
<li class="md-nav__item">
<a href="../" class="md-nav__link">
Model Summaries
</a>
</li>
<li class="md-nav__item md-nav__item--active md-nav__item--nested">
<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
<label class="md-nav__link" for="__nav_3">
Model Pages
<span class="md-nav__icon md-icon"></span>
</label>
<nav class="md-nav" aria-label="Model Pages" data-md-level="1">
<label class="md-nav__title" for="__nav_3">
<span class="md-nav__icon md-icon"></span>
Model Pages
</label>
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="../adversarial-inception-v3/" class="md-nav__link">
Adversarial Inception v3
</a>
</li>
<li class="md-nav__item">
<a href="../advprop/" class="md-nav__link">
AdvProp (EfficientNet)
</a>
</li>
<li class="md-nav__item">
<a href="../big-transfer/" class="md-nav__link">
Big Transfer (BiT)
</a>
</li>
<li class="md-nav__item">
<a href="../csp-darknet/" class="md-nav__link">
CSP-DarkNet
</a>
</li>
<li class="md-nav__item">
<a href="../csp-resnet/" class="md-nav__link">
CSP-ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../csp-resnext/" class="md-nav__link">
CSP-ResNeXt
</a>
</li>
<li class="md-nav__item">
<a href="../densenet/" class="md-nav__link">
DenseNet
</a>
</li>
<li class="md-nav__item">
<a href="../dla/" class="md-nav__link">
Deep Layer Aggregation
</a>
</li>
<li class="md-nav__item md-nav__item--active">
<input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
<label class="md-nav__link md-nav__link--active" for="__toc">
Dual Path Network (DPN)
<span class="md-nav__icon md-icon"></span>
</label>
<a href="./" class="md-nav__link md-nav__link--active">
Dual Path Network (DPN)
</a>
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
<label class="md-nav__title" for="__toc">
<span class="md-nav__icon md-icon"></span>
Table of contents
</label>
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
<li class="md-nav__item">
<a href="#how-do-i-use-this-model-on-an-image" class="md-nav__link">
How do I use this model on an image?
</a>
</li>
<li class="md-nav__item">
<a href="#how-do-i-finetune-this-model" class="md-nav__link">
How do I finetune this model?
</a>
</li>
<li class="md-nav__item">
<a href="#how-do-i-train-this-model" class="md-nav__link">
How do I train this model?
</a>
</li>
<li class="md-nav__item">
<a href="#citation" class="md-nav__link">
Citation
</a>
</li>
</ul>
</nav>
</li>
<li class="md-nav__item">
<a href="../ecaresnet/" class="md-nav__link">
ECA-ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../efficientnet-pruned/" class="md-nav__link">
EfficientNet (Knapsack Pruned)
</a>
</li>
<li class="md-nav__item">
<a href="../efficientnet/" class="md-nav__link">
EfficientNet
</a>
</li>
<li class="md-nav__item">
<a href="../ensemble-adversarial/" class="md-nav__link">
Ensemble Adversarial Inception ResNet v2
</a>
</li>
<li class="md-nav__item">
<a href="../ese-vovnet/" class="md-nav__link">
ESE-VoVNet
</a>
</li>
<li class="md-nav__item">
<a href="../fbnet/" class="md-nav__link">
FBNet
</a>
</li>
<li class="md-nav__item">
<a href="../gloun-inception-v3/" class="md-nav__link">
(Gluon) Inception v3
</a>
</li>
<li class="md-nav__item">
<a href="../gloun-resnet/" class="md-nav__link">
(Gluon) ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../gloun-resnext/" class="md-nav__link">
(Gluon) ResNeXt
</a>
</li>
<li class="md-nav__item">
<a href="../gloun-senet/" class="md-nav__link">
(Gluon) SENet
</a>
</li>
<li class="md-nav__item">
<a href="../gloun-seresnext/" class="md-nav__link">
(Gluon) SE-ResNeXt
</a>
</li>
<li class="md-nav__item">
<a href="../gloun-xception/" class="md-nav__link">
(Gluon) Xception
</a>
</li>
<li class="md-nav__item">
<a href="../hrnet/" class="md-nav__link">
HRNet
</a>
</li>
<li class="md-nav__item">
<a href="../ig-resnext/" class="md-nav__link">
Instagram ResNeXt WSL
</a>
</li>
<li class="md-nav__item">
<a href="../inception-resnet-v2/" class="md-nav__link">
Inception ResNet v2
</a>
</li>
<li class="md-nav__item">
<a href="../inception-v3/" class="md-nav__link">
Inception v3
</a>
</li>
<li class="md-nav__item">
<a href="../inception-v4/" class="md-nav__link">
Inception v4
</a>
</li>
<li class="md-nav__item">
<a href="../legacy-se-resnet/" class="md-nav__link">
(Legacy) SE-ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../legacy-se-resnext/" class="md-nav__link">
(Legacy) SE-ResNeXt
</a>
</li>
<li class="md-nav__item">
<a href="../legacy-senet/" class="md-nav__link">
(Legacy) SENet
</a>
</li>
<li class="md-nav__item">
<a href="../mixnet/" class="md-nav__link">
MixNet
</a>
</li>
<li class="md-nav__item">
<a href="../mnasnet/" class="md-nav__link">
MnasNet
</a>
</li>
<li class="md-nav__item">
<a href="../mobilenet-v2/" class="md-nav__link">
MobileNet v2
</a>
</li>
<li class="md-nav__item">
<a href="../mobilenet-v3/" class="md-nav__link">
MobileNet v3
</a>
</li>
<li class="md-nav__item">
<a href="../nasnet/" class="md-nav__link">
NASNet
</a>
</li>
<li class="md-nav__item">
<a href="../noisy-student/" class="md-nav__link">
Noisy Student (EfficientNet)
</a>
</li>
<li class="md-nav__item">
<a href="../pnasnet/" class="md-nav__link">
PNASNet
</a>
</li>
<li class="md-nav__item">
<a href="../regnetx/" class="md-nav__link">
RegNetX
</a>
</li>
<li class="md-nav__item">
<a href="../regnety/" class="md-nav__link">
RegNetY
</a>
</li>
<li class="md-nav__item">
<a href="../res2net/" class="md-nav__link">
Res2Net
</a>
</li>
<li class="md-nav__item">
<a href="../res2next/" class="md-nav__link">
Res2NeXt
</a>
</li>
<li class="md-nav__item">
<a href="../resnest/" class="md-nav__link">
ResNeSt
</a>
</li>
<li class="md-nav__item">
<a href="../resnet-d/" class="md-nav__link">
ResNet-D
</a>
</li>
<li class="md-nav__item">
<a href="../resnet/" class="md-nav__link">
ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../resnext/" class="md-nav__link">
ResNeXt
</a>
</li>
<li class="md-nav__item">
<a href="../rexnet/" class="md-nav__link">
RexNet
</a>
</li>
<li class="md-nav__item">
<a href="../se-resnet/" class="md-nav__link">
SE-ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../selecsls/" class="md-nav__link">
SelecSLS
</a>
</li>
<li class="md-nav__item">
<a href="../seresnext/" class="md-nav__link">
SE-ResNeXt
</a>
</li>
<li class="md-nav__item">
<a href="../skresnet/" class="md-nav__link">
SK-ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../skresnext/" class="md-nav__link">
SK-ResNeXt
</a>
</li>
<li class="md-nav__item">
<a href="../spnasnet/" class="md-nav__link">
SPNASNet
</a>
</li>
<li class="md-nav__item">
<a href="../ssl-resnet/" class="md-nav__link">
SSL ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../ssl-resnext/" class="md-nav__link">
SSL ResNeXT
</a>
</li>
<li class="md-nav__item">
<a href="../swsl-resnet/" class="md-nav__link">
SWSL ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../swsl-resnext/" class="md-nav__link">
SWSL ResNeXt
</a>
</li>
<li class="md-nav__item">
<a href="../tf-efficientnet-condconv/" class="md-nav__link">
(Tensorflow) EfficientNet CondConv
</a>
</li>
<li class="md-nav__item">
<a href="../tf-efficientnet-lite/" class="md-nav__link">
(Tensorflow) EfficientNet Lite
</a>
</li>
<li class="md-nav__item">
<a href="../tf-efficientnet/" class="md-nav__link">
(Tensorflow) EfficientNet
</a>
</li>
<li class="md-nav__item">
<a href="../tf-inception-v3/" class="md-nav__link">
(Tensorflow) Inception v3
</a>
</li>
<li class="md-nav__item">
<a href="../tf-mixnet/" class="md-nav__link">
(Tensorflow) MixNet
</a>
</li>
<li class="md-nav__item">
<a href="../tf-mobilenet-v3/" class="md-nav__link">
(Tensorflow) MobileNet v3
</a>
</li>
<li class="md-nav__item">
<a href="../tresnet/" class="md-nav__link">
TResNet
</a>
</li>
<li class="md-nav__item">
<a href="../vision-transformer/" class="md-nav__link">
Vision Transformer (ViT)
</a>
</li>
<li class="md-nav__item">
<a href="../wide-resnet/" class="md-nav__link">
Wide ResNet
</a>
</li>
<li class="md-nav__item">
<a href="../xception/" class="md-nav__link">
Xception
</a>
</li>
</ul>
</nav>
</li>
<li class="md-nav__item">
<a href="../../results/" class="md-nav__link">
Results
</a>
</li>
<li class="md-nav__item">
<a href="../../scripts/" class="md-nav__link">
Scripts
</a>
</li>
<li class="md-nav__item">
<a href="../../training_hparam_examples/" class="md-nav__link">
Training Examples
</a>
</li>
<li class="md-nav__item">
<a href="../../feature_extraction/" class="md-nav__link">
Feature Extraction
</a>
</li>
<li class="md-nav__item">
<a href="../../changes/" class="md-nav__link">
Recent Changes
</a>
</li>
<li class="md-nav__item">
<a href="../../archived_changes/" class="md-nav__link">
Archived Changes
</a>
</li>
</ul>
</nav>
</div>
</div>
</div>
<div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
<div class="md-sidebar__scrollwrap">
<div class="md-sidebar__inner">
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
<label class="md-nav__title" for="__toc">
<span class="md-nav__icon md-icon"></span>
Table of contents
</label>
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
<li class="md-nav__item">
<a href="#how-do-i-use-this-model-on-an-image" class="md-nav__link">
How do I use this model on an image?
</a>
</li>
<li class="md-nav__item">
<a href="#how-do-i-finetune-this-model" class="md-nav__link">
How do I finetune this model?
</a>
</li>
<li class="md-nav__item">
<a href="#how-do-i-train-this-model" class="md-nav__link">
How do I train this model?
</a>
</li>
<li class="md-nav__item">
<a href="#citation" class="md-nav__link">
Citation
</a>
</li>
</ul>
</nav>
</div>
</div>
</div>
<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<a href="https://github.com/rwightman/pytorch-image-models/edit/master/docs/models/dpn.md" title="Edit this page" class="md-content__button md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
</a>
<h1 id="dual-path-network-dpn">Dual Path Network (DPN)</h1>
<p>A <strong>Dual Path Network (DPN)</strong> is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that <a href="https://paperswithcode.com/method/resnet">ResNets</a> enables feature re-usage while DenseNet enables new feature exploration, and both are important for learning good representations. To enjoy the benefits from both path topologies, Dual Path Networks share common features while maintaining the flexibility to explore new features through dual path architectures. </p>
<p>The principal building block is an <a href="https://paperswithcode.com/method/dpn-block">DPN Block</a>.</p>
<h2 id="how-do-i-use-this-model-on-an-image">How do I use this model on an image?</h2>
<p>To load a pretrained model:</p>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">timm</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;dpn107&#39;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
</code></pre></div>
<p>To load and preprocess the image:
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">urllib</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">timm.data</span> <span class="kn">import</span> <span class="n">resolve_data_config</span>
<span class="kn">from</span> <span class="nn">timm.data.transforms_factory</span> <span class="kn">import</span> <span class="n">create_transform</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">resolve_data_config</span><span class="p">({},</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">create_transform</span><span class="p">(</span><span class="o">**</span><span class="n">config</span><span class="p">)</span>
<span class="n">url</span><span class="p">,</span> <span class="n">filename</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;https://github.com/pytorch/hub/raw/master/images/dog.jpg&quot;</span><span class="p">,</span> <span class="s2">&quot;dog.jpg&quot;</span><span class="p">)</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">&#39;RGB&#39;</span><span class="p">)</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># transform and add batch dimension</span>
</code></pre></div></p>
<p>To get the model predictions:
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
<span class="n">probabilities</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">probabilities</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># prints: torch.Size([1000])</span>
</code></pre></div></p>
<p>To get the top-5 predictions class names:
<div class="highlight"><pre><span></span><code><span class="c1"># Get imagenet class mappings</span>
<span class="n">url</span><span class="p">,</span> <span class="n">filename</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;imagenet_classes.txt&quot;</span><span class="p">)</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;imagenet_classes.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()]</span>
<span class="c1"># Print top categories per image</span>
<span class="n">top5_prob</span><span class="p">,</span> <span class="n">top5_catid</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">probabilities</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">top5_prob</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">categories</span><span class="p">[</span><span class="n">top5_catid</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">top5_prob</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
<span class="c1"># prints class names and probabilities like:</span>
<span class="c1"># [(&#39;Samoyed&#39;, 0.6425196528434753), (&#39;Pomeranian&#39;, 0.04062102362513542), (&#39;keeshond&#39;, 0.03186424449086189), (&#39;white wolf&#39;, 0.01739676296710968), (&#39;Eskimo dog&#39;, 0.011717947199940681)]</span>
</code></pre></div></p>
<p>Replace the model name with the variant you want to use, e.g. <code>dpn107</code>. You can find the IDs in the model summaries at the top of this page.</p>
<p>To extract image features with this model, follow the <a href="https://rwightman.github.io/pytorch-image-models/feature_extraction/">timm feature extraction examples</a>, just change the name of the model you want to use.</p>
<h2 id="how-do-i-finetune-this-model">How do I finetune this model?</h2>
<p>You can finetune any of the pre-trained models just by changing the classifier (the last layer).
<div class="highlight"><pre><span></span><code><span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;dpn107&#39;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">NUM_FINETUNE_CLASSES</span><span class="p">)</span>
</code></pre></div>
To finetune on your own dataset, you have to write a training loop or adapt <a href="https://github.com/rwightman/pytorch-image-models/blob/master/train.py">timm's training
script</a> to use your dataset.</p>
<h2 id="how-do-i-train-this-model">How do I train this model?</h2>
<p>You can follow the <a href="https://rwightman.github.io/pytorch-image-models/scripts/">timm recipe scripts</a> for training a new model afresh.</p>
<h2 id="citation">Citation</h2>
<div class="highlight"><pre><span></span><code><span class="nc">@misc</span><span class="p">{</span><span class="nl">chen2017dual</span><span class="p">,</span>
<span class="na">title</span><span class="p">=</span><span class="s">{Dual Path Networks}</span><span class="p">,</span>
<span class="na">author</span><span class="p">=</span><span class="s">{Yunpeng Chen and Jianan Li and Huaxin Xiao and Xiaojie Jin and Shuicheng Yan and Jiashi Feng}</span><span class="p">,</span>
<span class="na">year</span><span class="p">=</span><span class="s">{2017}</span><span class="p">,</span>
<span class="na">eprint</span><span class="p">=</span><span class="s">{1707.01629}</span><span class="p">,</span>
<span class="na">archivePrefix</span><span class="p">=</span><span class="s">{arXiv}</span><span class="p">,</span>
<span class="na">primaryClass</span><span class="p">=</span><span class="s">{cs.CV}</span>
<span class="p">}</span>
</code></pre></div>
<!--
Type: model-index
Collections:
- Name: DPN
Paper:
Title: Dual Path Networks
URL: https://paperswithcode.com/paper/dual-path-networks
Models:
- Name: dpn107
In Collection: DPN
Metadata:
FLOPs: 23524280296
Parameters: 86920000
File Size: 348612331
Architecture:
- Batch Normalization
- Convolution
- DPN Block
- Dense Connections
- Global Average Pooling
- Max Pooling
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 40x K80 GPUs
ID: dpn107
LR: 0.316
Layers: 107
Crop Pct: '0.875'
Batch Size: 1280
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dpn.py#L310
Weights: https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn107_extra-1ac7121e2.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.16%
Top 5 Accuracy: 94.91%
- Name: dpn131
In Collection: DPN
Metadata:
FLOPs: 20586274792
Parameters: 79250000
File Size: 318016207
Architecture:
- Batch Normalization
- Convolution
- DPN Block
- Dense Connections
- Global Average Pooling
- Max Pooling
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 40x K80 GPUs
ID: dpn131
LR: 0.316
Layers: 131
Crop Pct: '0.875'
Batch Size: 960
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dpn.py#L302
Weights: https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn131-71dfe43e0.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.83%
Top 5 Accuracy: 94.71%
- Name: dpn68
In Collection: DPN
Metadata:
FLOPs: 2990567880
Parameters: 12610000
File Size: 50761994
Architecture:
- Batch Normalization
- Convolution
- DPN Block
- Dense Connections
- Global Average Pooling
- Max Pooling
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 40x K80 GPUs
ID: dpn68
LR: 0.316
Layers: 68
Crop Pct: '0.875'
Batch Size: 1280
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dpn.py#L270
Weights: https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68-66bebafa7.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.31%
Top 5 Accuracy: 92.97%
- Name: dpn68b
In Collection: DPN
Metadata:
FLOPs: 2990567880
Parameters: 12610000
File Size: 50781025
Architecture:
- Batch Normalization
- Convolution
- DPN Block
- Dense Connections
- Global Average Pooling
- Max Pooling
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 40x K80 GPUs
ID: dpn68b
LR: 0.316
Layers: 68
Crop Pct: '0.875'
Batch Size: 1280
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dpn.py#L278
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dpn68b_ra-a31ca160.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.21%
Top 5 Accuracy: 94.42%
- Name: dpn92
In Collection: DPN
Metadata:
FLOPs: 8357659624
Parameters: 37670000
File Size: 151248422
Architecture:
- Batch Normalization
- Convolution
- DPN Block
- Dense Connections
- Global Average Pooling
- Max Pooling
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 40x K80 GPUs
ID: dpn92
LR: 0.316
Layers: 92
Crop Pct: '0.875'
Batch Size: 1280
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dpn.py#L286
Weights: https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn92_extra-b040e4a9b.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.99%
Top 5 Accuracy: 94.84%
- Name: dpn98
In Collection: DPN
Metadata:
FLOPs: 15003675112
Parameters: 61570000
File Size: 247021307
Architecture:
- Batch Normalization
- Convolution
- DPN Block
- Dense Connections
- Global Average Pooling
- Max Pooling
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 40x K80 GPUs
ID: dpn98
LR: 0.4
Layers: 98
Crop Pct: '0.875'
Batch Size: 1280
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dpn.py#L294
Weights: https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn98-5b90dec4d.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.65%
Top 5 Accuracy: 94.61%
-->
</article>
</div>
</div>
</main>
<footer class="md-footer">
<nav class="md-footer__inner md-grid" aria-label="Footer">
<a href="../dla/" class="md-footer__link md-footer__link--prev" rel="prev">
<div class="md-footer__button md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12z"/></svg>
</div>
<div class="md-footer__title">
<div class="md-ellipsis">
<span class="md-footer__direction">
Previous
</span>
Deep Layer Aggregation
</div>
</div>
</a>
<a href="../ecaresnet/" class="md-footer__link md-footer__link--next" rel="next">
<div class="md-footer__title">
<div class="md-ellipsis">
<span class="md-footer__direction">
Next
</span>
ECA-ResNet
</div>
</div>
<div class="md-footer__button md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4z"/></svg>
</div>
</a>
</nav>
<div class="md-footer-meta md-typeset">
<div class="md-footer-meta__inner md-grid">
<div class="md-footer-copyright">
Made with
<a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener">
Material for MkDocs
</a>
</div>
</div>
</div>
</footer>
</div>
<div class="md-dialog" data-md-component="dialog">
<div class="md-dialog__inner md-typeset"></div>
</div>
<script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copy": "Copy to clipboard", "clipboard.copied": "Copied to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.placeholder": "Type to start searching", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.term.missing": "Missing"}, "search": "../../assets/javascripts/workers/search.fb4a9340.min.js", "version": null}</script>
<script src="../../assets/javascripts/bundle.a1c7c35e.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/tablesort/5.2.1/tablesort.min.js"></script>
<script src="../../javascripts/tables.js"></script>
</body>
</html>
Reference in new issue View Git Blame Copy Permalink