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 'da8447c4ae'
${ noResults }
pytorch-image-models/models/index.html

993 lines
40 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="shortcut icon" href="../assets/images/favicon.png">
<meta name="generator" content="mkdocs-1.1.2, mkdocs-material-5.4.0">
<title>Model Architectures - Pytorch Image Models</title>
<link rel="stylesheet" href="../assets/stylesheets/main.fe0cca5b.min.css">
<link href="https://fonts.gstatic.com" rel="preconnect" crossorigin>
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,400i,700%7CRoboto+Mono&display=fallback">
<style>body,input{font-family:"Roboto",-apple-system,BlinkMacSystemFont,Helvetica,Arial,sans-serif}code,kbd,pre{font-family:"Roboto Mono",SFMono-Regular,Consolas,Menlo,monospace}</style>
</head>
<body dir="ltr">
<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="#model-architectures" 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-nav md-grid" aria-label="Header">
<a href=".." title="Pytorch Image Models" class="md-header-nav__button md-logo" aria-label="Pytorch Image Models">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 003-3 3 3 0 00-3-3 3 3 0 00-3 3 3 3 0 003 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-nav__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-nav__title" data-md-component="header-title">
<div class="md-header-nav__ellipsis">
<span class="md-header-nav__topic md-ellipsis">
Pytorch Image Models
</span>
<span class="md-header-nav__topic md-ellipsis">
Model Architectures
</span>
</div>
</div>
<label class="md-header-nav__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 0116 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 0 019.5 16 6.5 6.5 0 013 9.5 6.5 6.5 0 019.5 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">
<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 0116 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 0 019.5 16 6.5 6.5 0 013 9.5 6.5 6.5 0 019.5 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" data-md-component="search-reset" 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-nav__source">
<a href="https://github.com/rwightman/pytorch-image-models/" title="Go to repository" class="md-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 00-40.81 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 01-48.55 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 0 000 40.81l195.61 195.6a28.86 28.86 0 0040.8 0l194.69-194.69a28.86 28.86 0 000-40.81z"/></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="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">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 003-3 3 3 0 00-3-3 3 3 0 00-3 3 3 3 0 003 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">
<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 00-40.81 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 01-48.55 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 0 000 40.81l195.61 195.6a28.86 28.86 0 0040.8 0l194.69-194.69a28.86 28.86 0 000-40.81z"/></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=".." title="Getting Started" class="md-nav__link">
Getting Started
</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">
Model Architectures
<span class="md-nav__icon md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 9h14V7H3v2m0 4h14v-2H3v2m0 4h14v-2H3v2m16 0h2v-2h-2v2m0-10v2h2V7h-2m0 6h2v-2h-2v2z"/></svg>
</span>
</label>
<a href="./" title="Model Architectures" class="md-nav__link md-nav__link--active">
Model Architectures
</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">
<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>
</span>
Table of contents
</label>
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="#cross-stage-partial-networks-cspnetpy" class="md-nav__link">
Cross-Stage Partial Networks [cspnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#densenet-densenetpy" class="md-nav__link">
DenseNet [densenet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#dla-dlapy" class="md-nav__link">
DLA [dla.py]
</a>
</li>
<li class="md-nav__item">
<a href="#dual-path-networks-dpnpy" class="md-nav__link">
Dual-Path Networks [dpn.py]
</a>
</li>
<li class="md-nav__item">
<a href="#hrnet-hrnetpy" class="md-nav__link">
HRNet [hrnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#inception-v3-inception_v3py" class="md-nav__link">
Inception-V3 [inception_v3.py]
</a>
</li>
<li class="md-nav__item">
<a href="#inception-v4-inception_v4py" class="md-nav__link">
Inception-V4 [inception_v4.py]
</a>
</li>
<li class="md-nav__item">
<a href="#inception-resnet-v2-inception_resnet_v2py" class="md-nav__link">
Inception-ResNet-V2 [inception_resnet_v2.py]
</a>
</li>
<li class="md-nav__item">
<a href="#nasnet-a-nasnetpy" class="md-nav__link">
NASNet-A [nasnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#pnasnet-5-pnasnetpy" class="md-nav__link">
PNasNet-5 [pnasnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#efficientnet-efficientnetpy" class="md-nav__link">
EfficientNet [efficientnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#mobilenet-v3-mobilenetv3py" class="md-nav__link">
MobileNet-V3 [mobilenetv3.py]
</a>
</li>
<li class="md-nav__item">
<a href="#regnet-regnetpy" class="md-nav__link">
RegNet [regnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#resnet-resnext-resnetpy" class="md-nav__link">
ResNet, ResNeXt [resnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#res2net-res2netpy" class="md-nav__link">
Res2Net [res2net.py]
</a>
</li>
<li class="md-nav__item">
<a href="#resnest-resnestpy" class="md-nav__link">
ResNeSt [resnest.py]
</a>
</li>
<li class="md-nav__item">
<a href="#rexnet-rexnetpy" class="md-nav__link">
ReXNet [rexnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#selective-kernel-networks-sknetpy" class="md-nav__link">
Selective-Kernel Networks [sknet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#selecsls-selecslspy" class="md-nav__link">
SelecSLS [selecsls.py]
</a>
</li>
<li class="md-nav__item">
<a href="#squeeze-and-excitation-networks-senetpy" class="md-nav__link">
Squeeze-and-Excitation Networks [senet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#tresnet-tresnetpy" class="md-nav__link">
TResNet [tresnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#vision-transformer-vision_transformerpy" class="md-nav__link">
Vision Transformer [vision_transformer.py]
</a>
</li>
<li class="md-nav__item">
<a href="#vovnet-v2-and-v1-vovnetpy" class="md-nav__link">
VovNet V2 and V1 [vovnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#xception-xceptionpy" class="md-nav__link">
Xception [xception.py]
</a>
</li>
<li class="md-nav__item">
<a href="#xception-modified-aligned-gluon-gluon_xceptionpy" class="md-nav__link">
Xception (Modified Aligned, Gluon) [gluon_xception.py]
</a>
</li>
<li class="md-nav__item">
<a href="#xception-modified-aligned-tf-aligned_xceptionpy" class="md-nav__link">
Xception (Modified Aligned, TF) [aligned_xception.py]
</a>
</li>
</ul>
</nav>
</li>
<li class="md-nav__item">
<a href="../results/" title="Results" class="md-nav__link">
Results
</a>
</li>
<li class="md-nav__item">
<a href="../scripts/" title="Scripts" class="md-nav__link">
Scripts
</a>
</li>
<li class="md-nav__item">
<a href="../training_hparam_examples/" title="Training Examples" class="md-nav__link">
Training Examples
</a>
</li>
<li class="md-nav__item">
<a href="../feature_extraction/" title="Feature Extraction" class="md-nav__link">
Feature Extraction
</a>
</li>
<li class="md-nav__item">
<a href="../changes/" title="Recent Changes" class="md-nav__link">
Recent Changes
</a>
</li>
<li class="md-nav__item">
<a href="../archived_changes/" title="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="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">
<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>
</span>
Table of contents
</label>
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="#cross-stage-partial-networks-cspnetpy" class="md-nav__link">
Cross-Stage Partial Networks [cspnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#densenet-densenetpy" class="md-nav__link">
DenseNet [densenet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#dla-dlapy" class="md-nav__link">
DLA [dla.py]
</a>
</li>
<li class="md-nav__item">
<a href="#dual-path-networks-dpnpy" class="md-nav__link">
Dual-Path Networks [dpn.py]
</a>
</li>
<li class="md-nav__item">
<a href="#hrnet-hrnetpy" class="md-nav__link">
HRNet [hrnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#inception-v3-inception_v3py" class="md-nav__link">
Inception-V3 [inception_v3.py]
</a>
</li>
<li class="md-nav__item">
<a href="#inception-v4-inception_v4py" class="md-nav__link">
Inception-V4 [inception_v4.py]
</a>
</li>
<li class="md-nav__item">
<a href="#inception-resnet-v2-inception_resnet_v2py" class="md-nav__link">
Inception-ResNet-V2 [inception_resnet_v2.py]
</a>
</li>
<li class="md-nav__item">
<a href="#nasnet-a-nasnetpy" class="md-nav__link">
NASNet-A [nasnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#pnasnet-5-pnasnetpy" class="md-nav__link">
PNasNet-5 [pnasnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#efficientnet-efficientnetpy" class="md-nav__link">
EfficientNet [efficientnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#mobilenet-v3-mobilenetv3py" class="md-nav__link">
MobileNet-V3 [mobilenetv3.py]
</a>
</li>
<li class="md-nav__item">
<a href="#regnet-regnetpy" class="md-nav__link">
RegNet [regnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#resnet-resnext-resnetpy" class="md-nav__link">
ResNet, ResNeXt [resnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#res2net-res2netpy" class="md-nav__link">
Res2Net [res2net.py]
</a>
</li>
<li class="md-nav__item">
<a href="#resnest-resnestpy" class="md-nav__link">
ResNeSt [resnest.py]
</a>
</li>
<li class="md-nav__item">
<a href="#rexnet-rexnetpy" class="md-nav__link">
ReXNet [rexnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#selective-kernel-networks-sknetpy" class="md-nav__link">
Selective-Kernel Networks [sknet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#selecsls-selecslspy" class="md-nav__link">
SelecSLS [selecsls.py]
</a>
</li>
<li class="md-nav__item">
<a href="#squeeze-and-excitation-networks-senetpy" class="md-nav__link">
Squeeze-and-Excitation Networks [senet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#tresnet-tresnetpy" class="md-nav__link">
TResNet [tresnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#vision-transformer-vision_transformerpy" class="md-nav__link">
Vision Transformer [vision_transformer.py]
</a>
</li>
<li class="md-nav__item">
<a href="#vovnet-v2-and-v1-vovnetpy" class="md-nav__link">
VovNet V2 and V1 [vovnet.py]
</a>
</li>
<li class="md-nav__item">
<a href="#xception-xceptionpy" class="md-nav__link">
Xception [xception.py]
</a>
</li>
<li class="md-nav__item">
<a href="#xception-modified-aligned-gluon-gluon_xceptionpy" class="md-nav__link">
Xception (Modified Aligned, Gluon) [gluon_xception.py]
</a>
</li>
<li class="md-nav__item">
<a href="#xception-modified-aligned-tf-aligned_xceptionpy" class="md-nav__link">
Xception (Modified Aligned, TF) [aligned_xception.py]
</a>
</li>
</ul>
</nav>
</div>
</div>
</div>
<div class="md-content">
<article class="md-content__inner md-typeset">
<a href="https://github.com/rwightman/pytorch-image-models/edit/master/docs/models.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="model-architectures">Model Architectures</h1>
<p>The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below.</p>
<p>Most included models have pretrained weights. The weights are either:</p>
<ol>
<li>from their original sources</li>
<li>ported by myself from their original impl in a different framework (e.g. Tensorflow models)</li>
<li>trained from scratch using the included training script</li>
</ol>
<p>The validation results for the pretrained weights can be found <a href="../results/">here</a></p>
<h2 id="cross-stage-partial-networks-cspnetpy">Cross-Stage Partial Networks [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/cspnet.py">cspnet.py</a>]</h2>
<ul>
<li>Paper: <code>CSPNet: A New Backbone that can Enhance Learning Capability of CNN</code> - <a href="https://arxiv.org/abs/1911.11929">https://arxiv.org/abs/1911.11929</a></li>
<li>Reference impl: <a href="https://github.com/WongKinYiu/CrossStagePartialNetworks">https://github.com/WongKinYiu/CrossStagePartialNetworks</a></li>
</ul>
<h2 id="densenet-densenetpy">DenseNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/densenet.py">densenet.py</a>]</h2>
<ul>
<li>Paper: <code>Densely Connected Convolutional Networks</code> - <a href="https://arxiv.org/abs/1608.06993">https://arxiv.org/abs/1608.06993</a></li>
<li>Code: <a href="https://github.com/pytorch/vision/tree/master/torchvision/models">https://github.com/pytorch/vision/tree/master/torchvision/models</a></li>
</ul>
<h2 id="dla-dlapy">DLA [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py">dla.py</a>]</h2>
<ul>
<li>Paper: <a href="https://arxiv.org/abs/1707.06484">https://arxiv.org/abs/1707.06484</a></li>
<li>Code: <a href="https://github.com/ucbdrive/dla">https://github.com/ucbdrive/dla</a></li>
</ul>
<h2 id="dual-path-networks-dpnpy">Dual-Path Networks [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dpn.py">dpn.py</a>]</h2>
<ul>
<li>Paper: <code>Dual Path Networks</code> - <a href="https://arxiv.org/abs/1707.01629">https://arxiv.org/abs/1707.01629</a></li>
<li>My PyTorch code: <a href="https://github.com/rwightman/pytorch-dpn-pretrained">https://github.com/rwightman/pytorch-dpn-pretrained</a></li>
<li>Reference code: <a href="https://github.com/cypw/DPNs">https://github.com/cypw/DPNs</a></li>
</ul>
<h2 id="hrnet-hrnetpy">HRNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/hrnet.py">hrnet.py</a>]</h2>
<ul>
<li>Paper: <code>Deep High-Resolution Representation Learning for Visual Recognition</code> - <a href="https://arxiv.org/abs/1908.07919">https://arxiv.org/abs/1908.07919</a></li>
<li>Code: <a href="https://github.com/HRNet/HRNet-Image-Classification">https://github.com/HRNet/HRNet-Image-Classification</a></li>
</ul>
<h2 id="inception-v3-inception_v3py">Inception-V3 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v3.py">inception_v3.py</a>]</h2>
<ul>
<li>Paper: <code>Rethinking the Inception Architecture for Computer Vision</code> - <a href="https://arxiv.org/abs/1512.00567">https://arxiv.org/abs/1512.00567</a></li>
<li>Code: <a href="https://github.com/pytorch/vision/tree/master/torchvision/models">https://github.com/pytorch/vision/tree/master/torchvision/models</a></li>
</ul>
<h2 id="inception-v4-inception_v4py">Inception-V4 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v4.py">inception_v4.py</a>]</h2>
<ul>
<li>Paper: <code>Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning</code> - <a href="https://arxiv.org/abs/1602.07261">https://arxiv.org/abs/1602.07261</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets">https://github.com/tensorflow/models/tree/master/research/slim/nets</a></li>
</ul>
<h2 id="inception-resnet-v2-inception_resnet_v2py">Inception-ResNet-V2 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_resnet_v2.py">inception_resnet_v2.py</a>]</h2>
<ul>
<li>Paper: <code>Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning</code> - <a href="https://arxiv.org/abs/1602.07261">https://arxiv.org/abs/1602.07261</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets">https://github.com/tensorflow/models/tree/master/research/slim/nets</a></li>
</ul>
<h2 id="nasnet-a-nasnetpy">NASNet-A [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/nasnet.py">nasnet.py</a>]</h2>
<ul>
<li>Papers: <code>Learning Transferable Architectures for Scalable Image Recognition</code> - <a href="https://arxiv.org/abs/1707.07012">https://arxiv.org/abs/1707.07012</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet">https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet</a></li>
</ul>
<h2 id="pnasnet-5-pnasnetpy">PNasNet-5 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/pnasnet.py">pnasnet.py</a>]</h2>
<ul>
<li>Papers: <code>Progressive Neural Architecture Search</code> - <a href="https://arxiv.org/abs/1712.00559">https://arxiv.org/abs/1712.00559</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet">https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet</a></li>
</ul>
<h2 id="efficientnet-efficientnetpy">EfficientNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py">efficientnet.py</a>]</h2>
<ul>
<li>Papers:<ul>
<li>EfficientNet NoisyStudent (B0-B7, L2) - <a href="https://arxiv.org/abs/1911.04252">https://arxiv.org/abs/1911.04252</a></li>
<li>EfficientNet AdvProp (B0-B8) - <a href="https://arxiv.org/abs/1911.09665">https://arxiv.org/abs/1911.09665</a></li>
<li>EfficientNet (B0-B7) - <a href="https://arxiv.org/abs/1905.11946">https://arxiv.org/abs/1905.11946</a></li>
<li>EfficientNet-EdgeTPU (S, M, L) - <a href="https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html">https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html</a></li>
<li>MixNet - <a href="https://arxiv.org/abs/1907.09595">https://arxiv.org/abs/1907.09595</a></li>
<li>MNASNet B1, A1 (Squeeze-Excite), and Small - <a href="https://arxiv.org/abs/1807.11626">https://arxiv.org/abs/1807.11626</a></li>
<li>MobileNet-V2 - <a href="https://arxiv.org/abs/1801.04381">https://arxiv.org/abs/1801.04381</a></li>
<li>FBNet-C - <a href="https://arxiv.org/abs/1812.03443">https://arxiv.org/abs/1812.03443</a></li>
<li>Single-Path NAS - <a href="https://arxiv.org/abs/1904.02877">https://arxiv.org/abs/1904.02877</a></li>
</ul>
</li>
<li>My PyTorch code: <a href="https://github.com/rwightman/gen-efficientnet-pytorch">https://github.com/rwightman/gen-efficientnet-pytorch</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet">https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet</a></li>
</ul>
<h2 id="mobilenet-v3-mobilenetv3py">MobileNet-V3 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py">mobilenetv3.py</a>]</h2>
<ul>
<li>Paper: <code>Searching for MobileNetV3</code> - <a href="https://arxiv.org/abs/1905.02244">https://arxiv.org/abs/1905.02244</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet">https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet</a></li>
</ul>
<h2 id="regnet-regnetpy">RegNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/regnet.py">regnet.py</a>]</h2>
<ul>
<li>Paper: <code>Designing Network Design Spaces</code> - <a href="https://arxiv.org/abs/2003.13678">https://arxiv.org/abs/2003.13678</a></li>
<li>Reference code: <a href="https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py">https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py</a></li>
</ul>
<h2 id="resnet-resnext-resnetpy">ResNet, ResNeXt [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnet.py">resnet.py</a>]</h2>
<ul>
<li>ResNet (V1B)<ul>
<li>Paper: <code>Deep Residual Learning for Image Recognition</code> - <a href="https://arxiv.org/abs/1512.03385">https://arxiv.org/abs/1512.03385</a></li>
<li>Code: <a href="https://github.com/pytorch/vision/tree/master/torchvision/models">https://github.com/pytorch/vision/tree/master/torchvision/models</a></li>
</ul>
</li>
<li>ResNeXt<ul>
<li>Paper: <code>Aggregated Residual Transformations for Deep Neural Networks</code> - <a href="https://arxiv.org/abs/1611.05431">https://arxiv.org/abs/1611.05431</a></li>
<li>Code: <a href="https://github.com/pytorch/vision/tree/master/torchvision/models">https://github.com/pytorch/vision/tree/master/torchvision/models</a></li>
</ul>
</li>
<li>'Bag of Tricks' / Gluon C, D, E, S ResNet variants<ul>
<li>Paper: <code>Bag of Tricks for Image Classification with CNNs</code> - <a href="https://arxiv.org/abs/1812.01187">https://arxiv.org/abs/1812.01187</a></li>
<li>Code: <a href="https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py">https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py</a></li>
</ul>
</li>
<li>Instagram pretrained / ImageNet tuned ResNeXt101<ul>
<li>Paper: <code>Exploring the Limits of Weakly Supervised Pretraining</code> - <a href="https://arxiv.org/abs/1805.00932">https://arxiv.org/abs/1805.00932</a></li>
<li>Weights: <a href="https://pytorch.org/hub/facebookresearch_WSL-Images_resnext">https://pytorch.org/hub/facebookresearch_WSL-Images_resnext</a> (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)</li>
</ul>
</li>
<li>Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet and ResNeXts<ul>
<li>Paper: <code>Billion-scale semi-supervised learning for image classification</code> - <a href="https://arxiv.org/abs/1905.00546">https://arxiv.org/abs/1905.00546</a></li>
<li>Weights: <a href="https://github.com/facebookresearch/semi-supervised-ImageNet1K-models">https://github.com/facebookresearch/semi-supervised-ImageNet1K-models</a> (NOTE: CC BY-NC 4.0 License, NOT commercial friendly)</li>
</ul>
</li>
<li>Squeeze-and-Excitation Networks<ul>
<li>Paper: <code>Squeeze-and-Excitation Networks</code> - <a href="https://arxiv.org/abs/1709.01507">https://arxiv.org/abs/1709.01507</a></li>
<li>Code: Added to ResNet base, this is current version going forward, old <code>senet.py</code> is being deprecated</li>
</ul>
</li>
<li>ECAResNet (ECA-Net)<ul>
<li>Paper: <code>ECA-Net: Efficient Channel Attention for Deep CNN</code> - <a href="https://arxiv.org/abs/1910.03151v4">https://arxiv.org/abs/1910.03151v4</a></li>
<li>Code: Added to ResNet base, ECA module contributed by @VRandme, reference <a href="https://github.com/BangguWu/ECANet">https://github.com/BangguWu/ECANet</a></li>
</ul>
</li>
</ul>
<h2 id="res2net-res2netpy">Res2Net [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/res2net.py">res2net.py</a>]</h2>
<ul>
<li>Paper: <code>Res2Net: A New Multi-scale Backbone Architecture</code> - <a href="https://arxiv.org/abs/1904.01169">https://arxiv.org/abs/1904.01169</a></li>
<li>Code: <a href="https://github.com/gasvn/Res2Net">https://github.com/gasvn/Res2Net</a></li>
</ul>
<h2 id="resnest-resnestpy">ResNeSt [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnest.py">resnest.py</a>]</h2>
<ul>
<li>Paper: <code>ResNeSt: Split-Attention Networks</code> - <a href="https://arxiv.org/abs/2004.08955">https://arxiv.org/abs/2004.08955</a></li>
<li>Code: <a href="https://github.com/zhanghang1989/ResNeSt">https://github.com/zhanghang1989/ResNeSt</a></li>
</ul>
<h2 id="rexnet-rexnetpy">ReXNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/rexnet.py">rexnet.py</a>]</h2>
<ul>
<li>Paper: <code>ReXNet: Diminishing Representational Bottleneck on CNN</code> - <a href="https://arxiv.org/abs/2007.00992">https://arxiv.org/abs/2007.00992</a></li>
<li>Code: <a href="https://github.com/clovaai/rexnet">https://github.com/clovaai/rexnet</a></li>
</ul>
<h2 id="selective-kernel-networks-sknetpy">Selective-Kernel Networks [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/sknet.py">sknet.py</a>]</h2>
<ul>
<li>Paper: <code>Selective-Kernel Networks</code> - <a href="https://arxiv.org/abs/1903.06586">https://arxiv.org/abs/1903.06586</a></li>
<li>Code: <a href="https://github.com/implus/SKNet">https://github.com/implus/SKNet</a>, <a href="https://github.com/clovaai/assembled-cnn">https://github.com/clovaai/assembled-cnn</a></li>
</ul>
<h2 id="selecsls-selecslspy">SelecSLS [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/selecsls.py">selecsls.py</a>]</h2>
<ul>
<li>Paper: <code>XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera</code> - <a href="https://arxiv.org/abs/1907.00837">https://arxiv.org/abs/1907.00837</a></li>
<li>Code: <a href="https://github.com/mehtadushy/SelecSLS-Pytorch">https://github.com/mehtadushy/SelecSLS-Pytorch</a></li>
</ul>
<h2 id="squeeze-and-excitation-networks-senetpy">Squeeze-and-Excitation Networks [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/senet.py">senet.py</a>]</h2>
<p>NOTE: I am deprecating this version of the networks, the new ones are part of <code>resnet.py</code></p>
<ul>
<li>Paper: <code>Squeeze-and-Excitation Networks</code> - <a href="https://arxiv.org/abs/1709.01507">https://arxiv.org/abs/1709.01507</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a> </li>
</ul>
<h2 id="tresnet-tresnetpy">TResNet [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tresnet.py">tresnet.py</a>]</h2>
<ul>
<li>Paper: <code>TResNet: High Performance GPU-Dedicated Architecture</code> - <a href="https://arxiv.org/abs/2003.13630">https://arxiv.org/abs/2003.13630</a></li>
<li>Code: <a href="https://github.com/mrT23/TResNet">https://github.com/mrT23/TResNet</a></li>
</ul>
<h2 id="vision-transformer-vision_transformerpy">Vision Transformer [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py">vision_transformer.py</a>]</h2>
<ul>
<li>Paper: <code>An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale</code> - <a href="https://arxiv.org/abs/2010.11929">https://arxiv.org/abs/2010.11929</a></li>
<li>Reference code and pretrained weights: <a href="https://github.com/google-research/vision_transformer">https://github.com/google-research/vision_transformer</a></li>
</ul>
<h2 id="vovnet-v2-and-v1-vovnetpy">VovNet V2 and V1 [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vovnet.py">vovnet.py</a>]</h2>
<ul>
<li>Paper: <code>CenterMask : Real-Time Anchor-Free Instance Segmentation</code> - <a href="https://arxiv.org/abs/1911.06667">https://arxiv.org/abs/1911.06667</a></li>
<li>Reference code: <a href="https://github.com/youngwanLEE/vovnet-detectron2">https://github.com/youngwanLEE/vovnet-detectron2</a></li>
</ul>
<h2 id="xception-xceptionpy">Xception [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/xception.py">xception.py</a>]</h2>
<ul>
<li>Paper: <code>Xception: Deep Learning with Depthwise Separable Convolutions</code> - <a href="https://arxiv.org/abs/1610.02357">https://arxiv.org/abs/1610.02357</a></li>
<li>Code: <a href="https://github.com/Cadene/pretrained-models.pytorch">https://github.com/Cadene/pretrained-models.pytorch</a></li>
</ul>
<h2 id="xception-modified-aligned-gluon-gluon_xceptionpy">Xception (Modified Aligned, Gluon) [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/gluon_xception.py">gluon_xception.py</a>]</h2>
<ul>
<li>Paper: <code>Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation</code> - <a href="https://arxiv.org/abs/1802.02611">https://arxiv.org/abs/1802.02611</a></li>
<li>Reference code: <a href="https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo">https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo</a>, <a href="https://github.com/jfzhang95/pytorch-deeplab-xception/">https://github.com/jfzhang95/pytorch-deeplab-xception/</a></li>
</ul>
<h2 id="xception-modified-aligned-tf-aligned_xceptionpy">Xception (Modified Aligned, TF) [<a href="https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/aligned_xception.py">aligned_xception.py</a>]</h2>
<ul>
<li>Paper: <code>Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation</code> - <a href="https://arxiv.org/abs/1802.02611">https://arxiv.org/abs/1802.02611</a></li>
<li>Reference code: <a href="https://github.com/tensorflow/models/tree/master/research/deeplab">https://github.com/tensorflow/models/tree/master/research/deeplab</a></li>
</ul>
</article>
</div>
</div>
</main>
<footer class="md-footer">
<div class="md-footer-nav">
<nav class="md-footer-nav__inner md-grid" aria-label="Footer">
<a href=".." title="Getting Started" class="md-footer-nav__link md-footer-nav__link--prev" rel="prev">
<div class="md-footer-nav__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-nav__title">
<div class="md-ellipsis">
<span class="md-footer-nav__direction">
Previous
</span>
Getting Started
</div>
</div>
</a>
<a href="../results/" title="Results" class="md-footer-nav__link md-footer-nav__link--next" rel="next">
<div class="md-footer-nav__title">
<div class="md-ellipsis">
<span class="md-footer-nav__direction">
Next
</span>
Results
</div>
</div>
<div class="md-footer-nav__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>
<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>
<script src="../assets/javascripts/vendor.d710d30a.min.js"></script>
<script src="../assets/javascripts/bundle.b39636ac.min.js"></script><script id="__lang" type="application/json">{"clipboard.copy": "Copy to clipboard", "clipboard.copied": "Copied to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.result.placeholder": "Type to start searching", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents"}</script>
<script>
app = initialize({
base: "..",
features: [],
search: Object.assign({
worker: "../assets/javascripts/worker/search.a68abb33.min.js"
}, typeof search !== "undefined" && search)
})
</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