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 'ef9970fd4d'
${ noResults }
pytorch-image-models/models/tf-efficientnet-lite/index.html

1564 lines
43 KiB
Raw Normal View History Unescape

Deployed ac9a2c9 with MkDocs version: 1.1.2
4 years ago
<!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>(Tensorflow) EfficientNet Lite - 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="#tensorflow-efficientnet-lite" 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">
(Tensorflow) EfficientNet Lite
</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 Architectures
</a>
</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>
<li class="md-nav__item md-nav__item--active md-nav__item--nested">
<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_9" type="checkbox" id="__nav_9" checked>
<label class="md-nav__link" for="__nav_9">
Models
<span class="md-nav__icon md-icon"></span>
</label>
<nav class="md-nav" aria-label="Models" data-md-level="1">
<label class="md-nav__title" for="__nav_9">
<span class="md-nav__icon md-icon"></span>
Models
</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">
<a href="../dpn/" class="md-nav__link">
Dual Path Network (DPN)
</a>
</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 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">
(Tensorflow) EfficientNet Lite
<span class="md-nav__icon md-icon"></span>
</label>
<a href="./" class="md-nav__link md-nav__link--active">
(Tensorflow) EfficientNet Lite
</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="../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>
</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/tf-efficientnet-lite.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="tensorflow-efficientnet-lite">(Tensorflow) EfficientNet Lite</h1>
<p><strong>EfficientNet</strong> is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a <em>compound coefficient</em>. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use <span><span class="MathJax_Preview">2^N</span><script type="math/tex">2^N</script></span> times more computational resources, then we can simply increase the network depth by <span><span class="MathJax_Preview">\alpha ^ N</span><script type="math/tex">\alpha ^ N</script></span>, width by <span><span class="MathJax_Preview">\beta ^ N</span><script type="math/tex">\beta ^ N</script></span>, and image size by <span><span class="MathJax_Preview">\gamma ^ N</span><script type="math/tex">\gamma ^ N</script></span>, where <span><span class="MathJax_Preview">\alpha, \beta, \gamma</span><script type="math/tex">\alpha, \beta, \gamma</script></span> are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient <span><span class="MathJax_Preview">\phi</span><script type="math/tex">\phi</script></span> to uniformly scales network width, depth, and resolution in a principled way.</p>
<p>The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.</p>
<p>The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of <a href="https://paperswithcode.com/method/mobilenetv2">MobileNetV2</a>.</p>
<p>EfficientNet-Lite makes EfficientNet more suitable for mobile devices by introducing <a href="https://paperswithcode.com/method/relu6">ReLU6</a> activation functions and removing <a href="https://paperswithcode.com/method/squeeze-and-excitation">squeeze-and-excitation blocks</a>.</p>
<p>The weights from this model were ported from <a href="https://github.com/tensorflow/tpu">Tensorflow/TPU</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;tf_efficientnet_lite0&#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>tf_efficientnet_lite0</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;tf_efficientnet_lite0&#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">tan2020efficientnet</span><span class="p">,</span>
<span class="na">title</span><span class="p">=</span><span class="s">{EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}</span><span class="p">,</span>
<span class="na">author</span><span class="p">=</span><span class="s">{Mingxing Tan and Quoc V. Le}</span><span class="p">,</span>
<span class="na">year</span><span class="p">=</span><span class="s">{2020}</span><span class="p">,</span>
<span class="na">eprint</span><span class="p">=</span><span class="s">{1905.11946}</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.LG}</span>
<span class="p">}</span>
</code></pre></div>
<!--
Type: model-index
Collections:
- Name: TF EfficientNet Lite
Paper:
Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
Models:
- Name: tf_efficientnet_lite0
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 488052032
Parameters: 4650000
File Size: 18820223
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite0
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1596
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.83%
Top 5 Accuracy: 92.17%
- Name: tf_efficientnet_lite1
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 773639520
Parameters: 5420000
File Size: 21939331
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite1
Crop Pct: '0.882'
Image Size: '240'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1607
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.67%
Top 5 Accuracy: 93.24%
- Name: tf_efficientnet_lite2
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 1068494432
Parameters: 6090000
File Size: 24658687
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite2
Crop Pct: '0.89'
Image Size: '260'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1618
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.48%
Top 5 Accuracy: 93.75%
- Name: tf_efficientnet_lite3
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 2011534304
Parameters: 8199999
File Size: 33161413
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite3
Crop Pct: '0.904'
Image Size: '300'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1629
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.83%
Top 5 Accuracy: 94.91%
- Name: tf_efficientnet_lite4
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 5164802912
Parameters: 13010000
File Size: 52558819
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite4
Crop Pct: '0.92'
Image Size: '380'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1640
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.54%
Top 5 Accuracy: 95.66%
-->
</article>
</div>
</div>
</main>
<footer class="md-footer">
<nav class="md-footer__inner md-grid" aria-label="Footer">
<a href="../tf-efficientnet-condconv/" 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>
(Tensorflow) EfficientNet CondConv
</div>
</div>
</a>
<a href="../tf-efficientnet/" 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>
(Tensorflow) EfficientNet
</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>