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<!DOCTYPE html>
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<title>torchvision.models — PyTorch 0.1.12_2 documentation</title>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../notes/autograd.html">Autograd mechanics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<li class="toctree-l3"><a class="reference internal" href="../nn.html#multilabelsoftmarginloss"><span class="hidden-section">MultiLabelSoftMarginLoss</span></a></li>
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<p class="caption"><span class="caption-text">torchvision Reference</span></p>
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<div class="section" id="module-torchvision.models">
<span id="torchvision-models"></span><h1>torchvision.models<a class="headerlink" href="#module-torchvision.models" title="Permalink to this headline">¶</a></h1>
<p>The models subpackage contains definitions for the following model
architectures:</p>
<ul class="simple">
<li><a class="reference external" href="https://arxiv.org/abs/1404.5997">AlexNet</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1409.1556">VGG</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a></li>
</ul>
<p>You can construct a model with random weights by calling its constructor:</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="n">resnet18</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">()</span>
<span class="n">alexnet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">()</span>
<span class="n">squeezenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">squeezenet1_0</span><span class="p">()</span>
<span class="n">densenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet_161</span><span class="p">()</span>
</pre></div>
</div>
<p>We provide pre-trained models for the ResNet variants and AlexNet, using the
PyTorch <a class="reference internal" href="../model_zoo.html#module-torch.utils.model_zoo" title="torch.utils.model_zoo"><code class="xref py py-mod docutils literal"><span class="pre">torch.utils.model_zoo</span></code></a>. These can constructed by passing
<code class="docutils literal"><span class="pre">pretrained=True</span></code>:</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="n">resnet18</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</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">alexnet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>ImageNet 1-crop error rates (224x224)</p>
<table border="1" class="docutils">
<colgroup>
<col width="48%" />
<col width="26%" />
<col width="26%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Network</th>
<th class="head">Top-1 error</th>
<th class="head">Top-5 error</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>ResNet-18</td>
<td>30.24</td>
<td>10.92</td>
</tr>
<tr class="row-odd"><td>ResNet-34</td>
<td>26.70</td>
<td>8.58</td>
</tr>
<tr class="row-even"><td>ResNet-50</td>
<td>23.85</td>
<td>7.13</td>
</tr>
<tr class="row-odd"><td>ResNet-101</td>
<td>22.63</td>
<td>6.44</td>
</tr>
<tr class="row-even"><td>ResNet-152</td>
<td>21.69</td>
<td>5.94</td>
</tr>
<tr class="row-odd"><td>Inception v3</td>
<td>22.55</td>
<td>6.44</td>
</tr>
<tr class="row-even"><td>AlexNet</td>
<td>43.45</td>
<td>20.91</td>
</tr>
<tr class="row-odd"><td>VGG-11</td>
<td>30.98</td>
<td>11.37</td>
</tr>
<tr class="row-even"><td>VGG-13</td>
<td>30.07</td>
<td>10.75</td>
</tr>
<tr class="row-odd"><td>VGG-16</td>
<td>28.41</td>
<td>9.62</td>
</tr>
<tr class="row-even"><td>VGG-19</td>
<td>27.62</td>
<td>9.12</td>
</tr>
<tr class="row-odd"><td>SqueezeNet 1.0</td>
<td>41.90</td>
<td>19.58</td>
</tr>
<tr class="row-even"><td>SqueezeNet 1.1</td>
<td>41.81</td>
<td>19.38</td>
</tr>
<tr class="row-odd"><td>Densenet-121</td>
<td>25.35</td>
<td>7.83</td>
</tr>
<tr class="row-even"><td>Densenet-169</td>
<td>24.00</td>
<td>7.00</td>
</tr>
<tr class="row-odd"><td>Densenet-201</td>
<td>22.80</td>
<td>6.43</td>
</tr>
<tr class="row-even"><td>Densenet-161</td>
<td>22.35</td>
<td>6.20</td>
</tr>
</tbody>
</table>
<dl class="function">
<dt id="torchvision.models.alexnet">
<code class="descclassname">torchvision.models.</code><code class="descname">alexnet</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.alexnet" title="Permalink to this definition">¶</a></dt>
<dd><p>AlexNet model architecture from the
<a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick…”</a> paper.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet18">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet18</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet18" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-18 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet34">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet34</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet34" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-34 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet50">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet50</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet50" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-50 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet101">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet101</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet101" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-101 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet152">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet152</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet152" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-152 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg11">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg11</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg11" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 11-layer model (configuration “A”)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg11_bn">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg11_bn</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg11_bn" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 11-layer model (configuration “A”) with batch normalization</p>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg13">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg13</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg13" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 13-layer model (configuration “B”)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg13_bn">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg13_bn</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg13_bn" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 13-layer model (configuration “B”) with batch normalization</p>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg16">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg16</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg16" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 16-layer model (configuration “D”)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg16_bn">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg16_bn</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg16_bn" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 16-layer model (configuration “D”) with batch normalization</p>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg19">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg19</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg19" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 19-layer model (configuration “E”)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg19_bn">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg19_bn</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg19_bn" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 19-layer model (configuration ‘E’) with batch normalization</p>
</dd></dl>
</div>
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