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<div class="section" id="torchvision-models">
<h1>torchvision.models<a class="headerlink" href="#torchvision-models" title="Permalink to this headline">¶</a></h1>
<p>The models subpackage contains definitions of models for addressing
different tasks, including: image classification, pixelwise semantic
segmentation, object detection, instance segmentation, person
keypoint detection and video classification.</p>
<div class="section" id="classification">
<h2>Classification<a class="headerlink" href="#classification" title="Permalink to this headline">¶</a></h2>
<p>The models subpackage contains definitions for the following model
architectures for image classification:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://arxiv.org/abs/1404.5997">AlexNet</a></p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1409.1556">VGG</a></p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet</a></p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a></p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a></p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1512.00567">Inception</a> v3</p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1409.4842">GoogLeNet</a></p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1807.11164">ShuffleNet</a> v2</p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1801.04381">MobileNet</a> v2</p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1611.05431">ResNeXt</a></p></li>
<li><p><a class="reference internal" href="#wide-resnet">Wide ResNet</a></p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1807.11626">MNASNet</a></p></li>
</ul>
<p>You can construct a model with random weights by calling its constructor:</p>
<div class="highlight-python notranslate"><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">vgg16</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16</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">densenet161</span><span class="p">()</span>
<span class="n">inception</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">inception_v3</span><span class="p">()</span>
<span class="n">googlenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">googlenet</span><span class="p">()</span>
<span class="n">shufflenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">shufflenet_v2_x1_0</span><span class="p">()</span>
<span class="n">mobilenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="p">()</span>
<span class="n">resnext50_32x4d</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnext50_32x4d</span><span class="p">()</span>
<span class="n">wide_resnet50_2</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">wide_resnet50_2</span><span class="p">()</span>
<span class="n">mnasnet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">mnasnet1_0</span><span class="p">()</span>
</pre></div>
</div>
<p>We provide pre-trained models, 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 notranslate"><span class="pre">torch.utils.model_zoo</span></code></a>.
These can be constructed by passing <code class="docutils literal notranslate"><span class="pre">pretrained=True</span></code>:</p>
<div class="highlight-python notranslate"><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>
<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">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">vgg16</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16</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">densenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet161</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">inception</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">inception_v3</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">googlenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">googlenet</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">shufflenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">shufflenet_v2_x1_0</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">mobilenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">mobilenet_v2</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">resnext50_32x4d</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnext50_32x4d</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">wide_resnet50_2</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">wide_resnet50_2</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">mnasnet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">mnasnet1_0</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>Instancing a pre-trained model will download its weights to a cache directory.
This directory can be set using the <cite>TORCH_MODEL_ZOO</cite> environment variable. See
<a class="reference internal" href="../model_zoo.html#torch.utils.model_zoo.load_url" title="torch.utils.model_zoo.load_url"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.utils.model_zoo.load_url()</span></code></a> for details.</p>
<p>Some models use modules which have different training and evaluation
behavior, such as batch normalization. To switch between these modes, use
<code class="docutils literal notranslate"><span class="pre">model.train()</span></code> or <code class="docutils literal notranslate"><span class="pre">model.eval()</span></code> as appropriate. See
<a class="reference internal" href="../generated/torch.nn.Module.html#torch.nn.Module.train" title="torch.nn.Module.train"><code class="xref py py-meth docutils literal notranslate"><span class="pre">train()</span></code></a> or <a class="reference internal" href="../generated/torch.nn.Module.html#torch.nn.Module.eval" title="torch.nn.Module.eval"><code class="xref py py-meth docutils literal notranslate"><span class="pre">eval()</span></code></a> for details.</p>
<p>All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape (3 x H x W),
where H and W are expected to be at least 224.
The images have to be loaded in to a range of [0, 1] and then normalized
using <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">=</span> <span class="pre">[0.485,</span> <span class="pre">0.456,</span> <span class="pre">0.406]</span></code> and <code class="docutils literal notranslate"><span class="pre">std</span> <span class="pre">=</span> <span class="pre">[0.229,</span> <span class="pre">0.224,</span> <span class="pre">0.225]</span></code>.
You can use the following transform to normalize:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">normalize</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
<span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
</pre></div>
</div>
<p>An example of such normalization can be found in the imagenet example
<a class="reference external" href="https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101">here</a></p>
<p>The process for obtaining the values of <cite>mean</cite> and <cite>std</cite> is roughly equivalent
to:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span> <span class="k">as</span> <span class="n">T</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">T</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span> <span class="n">T</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> <span class="n">T</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">()])</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageNet</span><span class="p">(</span><span class="s2">"."</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="s2">"train"</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
<span class="n">means</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">stds</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">subset</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
<span class="n">means</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">img</span><span class="p">))</span>
<span class="n">stds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">img</span><span class="p">))</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">means</span><span class="p">))</span>
<span class="n">std</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">stds</span><span class="p">))</span>
</pre></div>
</div>
<p>Unfortunately, the concrete <cite>subset</cite> that was used is lost. For more
information see <a class="reference external" href="https://github.com/pytorch/vision/issues/1439">this discussion</a>
or <a class="reference external" href="https://github.com/pytorch/vision/pull/1965">these experiments</a>.</p>
<p>ImageNet 1-crop error rates (224x224)</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Network</p></th>
<th class="head"><p>Top-1 error</p></th>
<th class="head"><p>Top-5 error</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>AlexNet</p></td>
<td><p>43.45</p></td>
<td><p>20.91</p></td>
</tr>
<tr class="row-odd"><td><p>VGG-11</p></td>
<td><p>30.98</p></td>
<td><p>11.37</p></td>
</tr>
<tr class="row-even"><td><p>VGG-13</p></td>
<td><p>30.07</p></td>
<td><p>10.75</p></td>
</tr>
<tr class="row-odd"><td><p>VGG-16</p></td>
<td><p>28.41</p></td>
<td><p>9.62</p></td>
</tr>
<tr class="row-even"><td><p>VGG-19</p></td>
<td><p>27.62</p></td>
<td><p>9.12</p></td>
</tr>
<tr class="row-odd"><td><p>VGG-11 with batch normalization</p></td>
<td><p>29.62</p></td>
<td><p>10.19</p></td>
</tr>
<tr class="row-even"><td><p>VGG-13 with batch normalization</p></td>
<td><p>28.45</p></td>
<td><p>9.63</p></td>
</tr>
<tr class="row-odd"><td><p>VGG-16 with batch normalization</p></td>
<td><p>26.63</p></td>
<td><p>8.50</p></td>
</tr>
<tr class="row-even"><td><p>VGG-19 with batch normalization</p></td>
<td><p>25.76</p></td>
<td><p>8.15</p></td>
</tr>
<tr class="row-odd"><td><p>ResNet-18</p></td>
<td><p>30.24</p></td>
<td><p>10.92</p></td>
</tr>
<tr class="row-even"><td><p>ResNet-34</p></td>
<td><p>26.70</p></td>
<td><p>8.58</p></td>
</tr>
<tr class="row-odd"><td><p>ResNet-50</p></td>
<td><p>23.85</p></td>
<td><p>7.13</p></td>
</tr>
<tr class="row-even"><td><p>ResNet-101</p></td>
<td><p>22.63</p></td>
<td><p>6.44</p></td>
</tr>
<tr class="row-odd"><td><p>ResNet-152</p></td>
<td><p>21.69</p></td>
<td><p>5.94</p></td>
</tr>
<tr class="row-even"><td><p>SqueezeNet 1.0</p></td>
<td><p>41.90</p></td>
<td><p>19.58</p></td>
</tr>
<tr class="row-odd"><td><p>SqueezeNet 1.1</p></td>
<td><p>41.81</p></td>
<td><p>19.38</p></td>
</tr>
<tr class="row-even"><td><p>Densenet-121</p></td>
<td><p>25.35</p></td>
<td><p>7.83</p></td>
</tr>
<tr class="row-odd"><td><p>Densenet-169</p></td>
<td><p>24.00</p></td>
<td><p>7.00</p></td>
</tr>
<tr class="row-even"><td><p>Densenet-201</p></td>
<td><p>22.80</p></td>
<td><p>6.43</p></td>
</tr>
<tr class="row-odd"><td><p>Densenet-161</p></td>
<td><p>22.35</p></td>
<td><p>6.20</p></td>
</tr>
<tr class="row-even"><td><p>Inception v3</p></td>
<td><p>22.55</p></td>
<td><p>6.44</p></td>
</tr>
<tr class="row-odd"><td><p>GoogleNet</p></td>
<td><p>30.22</p></td>
<td><p>10.47</p></td>
</tr>
<tr class="row-even"><td><p>ShuffleNet V2</p></td>
<td><p>30.64</p></td>
<td><p>11.68</p></td>
</tr>
<tr class="row-odd"><td><p>MobileNet V2</p></td>
<td><p>28.12</p></td>
<td><p>9.71</p></td>
</tr>
<tr class="row-even"><td><p>ResNeXt-50-32x4d</p></td>
<td><p>22.38</p></td>
<td><p>6.30</p></td>
</tr>
<tr class="row-odd"><td><p>ResNeXt-101-32x8d</p></td>
<td><p>20.69</p></td>
<td><p>5.47</p></td>
</tr>
<tr class="row-even"><td><p>Wide ResNet-50-2</p></td>
<td><p>21.49</p></td>
<td><p>5.91</p></td>
</tr>
<tr class="row-odd"><td><p>Wide ResNet-101-2</p></td>
<td><p>21.16</p></td>
<td><p>5.72</p></td>
</tr>
<tr class="row-even"><td><p>MNASNet 1.0</p></td>
<td><p>26.49</p></td>
<td><p>8.456</p></td>
</tr>
</tbody>
</table>
<div class="section" id="id1">
<h3>Alexnet<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torchvision.models.alexnet">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">alexnet</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/alexnet.html#alexnet"><span class="viewcode-link">[source]</span></a><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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="id2">
<h3>VGG<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torchvision.models.vgg11">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">vgg11</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/vgg.html#vgg11"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.vgg11" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 11-layer model (configuration “A”) from
<a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">“Very Deep Convolutional Networks For Large-Scale Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg11_bn">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">vgg11_bn</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/vgg.html#vgg11_bn"><span class="viewcode-link">[source]</span></a><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
<a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">“Very Deep Convolutional Networks For Large-Scale Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg13">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">vgg13</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/vgg.html#vgg13"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.vgg13" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 13-layer model (configuration “B”)
<a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">“Very Deep Convolutional Networks For Large-Scale Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg13_bn">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">vgg13_bn</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/vgg.html#vgg13_bn"><span class="viewcode-link">[source]</span></a><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
<a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">“Very Deep Convolutional Networks For Large-Scale Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg16">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">vgg16</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/vgg.html#vgg16"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.vgg16" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 16-layer model (configuration “D”)
<a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">“Very Deep Convolutional Networks For Large-Scale Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg16_bn">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">vgg16_bn</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/vgg.html#vgg16_bn"><span class="viewcode-link">[source]</span></a><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
<a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">“Very Deep Convolutional Networks For Large-Scale Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg19">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">vgg19</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/vgg.html#vgg19"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.vgg19" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 19-layer model (configuration “E”)
<a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">“Very Deep Convolutional Networks For Large-Scale Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg19_bn">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">vgg19_bn</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/vgg.html#vgg19_bn"><span class="viewcode-link">[source]</span></a><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
<a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">“Very Deep Convolutional Networks For Large-Scale Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="id10">
<h3>ResNet<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torchvision.models.resnet18">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">resnet18</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/resnet.html#resnet18"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.resnet18" title="Permalink to this definition">¶</a></dt>
<dd><p>ResNet-18 model from
<a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">“Deep Residual Learning for Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet34">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">resnet34</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/resnet.html#resnet34"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.resnet34" title="Permalink to this definition">¶</a></dt>
<dd><p>ResNet-34 model from
<a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">“Deep Residual Learning for Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet50">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">resnet50</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/resnet.html#resnet50"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.resnet50" title="Permalink to this definition">¶</a></dt>
<dd><p>ResNet-50 model from
<a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">“Deep Residual Learning for Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet101">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">resnet101</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/resnet.html#resnet101"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.resnet101" title="Permalink to this definition">¶</a></dt>
<dd><p>ResNet-101 model from
<a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">“Deep Residual Learning for Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet152">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">resnet152</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/resnet.html#resnet152"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.resnet152" title="Permalink to this definition">¶</a></dt>
<dd><p>ResNet-152 model from
<a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">“Deep Residual Learning for Image Recognition”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="id15">
<h3>SqueezeNet<a class="headerlink" href="#id15" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torchvision.models.squeezenet1_0">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">squeezenet1_0</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/squeezenet.html#squeezenet1_0"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.squeezenet1_0" title="Permalink to this definition">¶</a></dt>
<dd><p>SqueezeNet model architecture from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size”</a> paper.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.squeezenet1_1">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">squeezenet1_1</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/squeezenet.html#squeezenet1_1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.squeezenet1_1" title="Permalink to this definition">¶</a></dt>
<dd><p>SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="id16">
<h3>DenseNet<a class="headerlink" href="#id16" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torchvision.models.densenet121">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">densenet121</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/densenet.html#densenet121"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.densenet121" title="Permalink to this definition">¶</a></dt>
<dd><p>Densenet-121 model from
<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
<li><p><strong>memory_efficient</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – but slower. Default: <em>False</em>. See <a class="reference external" href="https://arxiv.org/pdf/1707.06990.pdf">“paper”</a></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.densenet169">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">densenet169</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/densenet.html#densenet169"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.densenet169" title="Permalink to this definition">¶</a></dt>
<dd><p>Densenet-169 model from
<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
<li><p><strong>memory_efficient</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – <p>but slower. Default: <em>False</em>. See <a class="reference external" href="https://arxiv.org/pdf/1707.06990.pdf">“paper”</a></p>
</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.densenet161">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">densenet161</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/densenet.html#densenet161"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.densenet161" title="Permalink to this definition">¶</a></dt>
<dd><p>Densenet-161 model from
<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
<li><p><strong>memory_efficient</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – <p>but slower. Default: <em>False</em>. See <a class="reference external" href="https://arxiv.org/pdf/1707.06990.pdf">“paper”</a></p>
</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.densenet201">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">densenet201</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/densenet.html#densenet201"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.densenet201" title="Permalink to this definition">¶</a></dt>
<dd><p>Densenet-201 model from
<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
<li><p><strong>memory_efficient</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – <p>but slower. Default: <em>False</em>. See <a class="reference external" href="https://arxiv.org/pdf/1707.06990.pdf">“paper”</a></p>
</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="inception-v3">
<h3>Inception v3<a class="headerlink" href="#inception-v3" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torchvision.models.inception_v3">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">inception_v3</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/inception.html#inception_v3"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.inception_v3" title="Permalink to this definition">¶</a></dt>
<dd><p>Inception v3 model architecture from
<a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><strong>Important</strong>: In contrast to the other models the inception_v3 expects tensors with a size of
N x 3 x 299 x 299, so ensure your images are sized accordingly.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
<li><p><strong>aux_logits</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, add an auxiliary branch that can improve training.
Default: <em>True</em></p></li>
<li><p><strong>transform_input</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, preprocesses the input according to the method with which it
was trained on ImageNet. Default: <em>False</em></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This requires <cite>scipy</cite> to be installed</p>
</div>
</div>
<div class="section" id="id23">
<h3>GoogLeNet<a class="headerlink" href="#id23" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torchvision.models.googlenet">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">googlenet</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/googlenet.html#googlenet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.googlenet" title="Permalink to this definition">¶</a></dt>
<dd><p>GoogLeNet (Inception v1) model architecture from
<a class="reference external" href="http://arxiv.org/abs/1409.4842">“Going Deeper with Convolutions”</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
<li><p><strong>aux_logits</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, adds two auxiliary branches that can improve training.
Default: <em>False</em> when pretrained is True otherwise <em>True</em></p></li>
<li><p><strong>transform_input</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, preprocesses the input according to the method with which it
was trained on ImageNet. Default: <em>False</em></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This requires <cite>scipy</cite> to be installed</p>
</div>
</div>
<div class="section" id="shufflenet-v2">
<h3>ShuffleNet v2<a class="headerlink" href="#shufflenet-v2" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torchvision.models.shufflenet_v2_x0_5">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">shufflenet_v2_x0_5</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/shufflenetv2.html#shufflenet_v2_x0_5"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.shufflenet_v2_x0_5" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ShuffleNetV2 with 0.5x output channels, as described in
<a class="reference external" href="https://arxiv.org/abs/1807.11164">“ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.shufflenet_v2_x1_0">
<code class="sig-prename descclassname">torchvision.models.</code><code class="sig-name descname">shufflenet_v2_x1_0</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">progress=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/models/shufflenetv2.html#shufflenet_v2_x1_0"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.models.shufflenet_v2_x1_0" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ShuffleNetV2 with 1.0x output channels, as described in
<a class="reference external" href="https://arxiv.org/abs/1807.11164">“ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</p></li>
<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – If True, displays a progress bar of the download to stderr</p></li>
</ul>