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<div class="section" id="torchvision-datasets">
<h1>torchvision.datasets<a class="headerlink" href="#torchvision-datasets" title="Permalink to this headline">¶</a></h1>
<p>All datasets are subclasses of <a class="reference internal" href="../data.html#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.Dataset</span></code></a>
i.e, they have <code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> and <code class="docutils literal notranslate"><span class="pre">__len__</span></code> methods implemented.
Hence, they can all be passed to a <a class="reference internal" href="../data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>
which can load multiple samples parallelly using <code class="docutils literal notranslate"><span class="pre">torch.multiprocessing</span></code> workers.
For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">imagenet_data</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="s1">'path/to/imagenet_root/'</span><span class="p">)</span>
<span class="n">data_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">imagenet_data</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">num_workers</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">nThreads</span><span class="p">)</span>
</pre></div>
</div>
<p>The following datasets are available:</p>
<div class="contents local topic" id="datasets">
<p class="topic-title first">Datasets</p>
<ul class="simple">
<li><a class="reference internal" href="#mnist" id="id7">MNIST</a></li>
<li><a class="reference internal" href="#fashion-mnist" id="id8">Fashion-MNIST</a></li>
<li><a class="reference internal" href="#emnist" id="id9">EMNIST</a></li>
<li><a class="reference internal" href="#coco" id="id10">COCO</a><ul>
<li><a class="reference internal" href="#captions" id="id11">Captions</a></li>
<li><a class="reference internal" href="#detection" id="id12">Detection</a></li>
</ul>
</li>
<li><a class="reference internal" href="#lsun" id="id13">LSUN</a></li>
<li><a class="reference internal" href="#imagefolder" id="id14">ImageFolder</a></li>
<li><a class="reference internal" href="#datasetfolder" id="id15">DatasetFolder</a></li>
<li><a class="reference internal" href="#imagenet-12" id="id16">Imagenet-12</a></li>
<li><a class="reference internal" href="#cifar" id="id17">CIFAR</a></li>
<li><a class="reference internal" href="#stl10" id="id18">STL10</a></li>
<li><a class="reference internal" href="#svhn" id="id19">SVHN</a></li>
<li><a class="reference internal" href="#phototour" id="id20">PhotoTour</a></li>
<li><a class="reference internal" href="#sbu" id="id21">SBU</a></li>
<li><a class="reference internal" href="#flickr" id="id22">Flickr</a></li>
<li><a class="reference internal" href="#voc" id="id23">VOC</a></li>
</ul>
</div>
<p>All the datasets have almost similar API. They all have two common arguments:
<code class="docutils literal notranslate"><span class="pre">transform</span></code> and <code class="docutils literal notranslate"><span class="pre">target_transform</span></code> to transform the input and target respectively.</p>
<div class="section" id="mnist">
<h2><a class="toc-backref" href="#id7">MNIST</a><a class="headerlink" href="#mnist" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.MNIST">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">MNIST</code><span class="sig-paren">(</span><em>root</em>, <em>train=True</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/mnist.html#MNIST"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.MNIST" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://yann.lecun.com/exdb/mnist/">MNIST</a> Dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory of dataset where <code class="docutils literal notranslate"><span class="pre">processed/training.pt</span></code>
and <code class="docutils literal notranslate"><span class="pre">processed/test.pt</span></code> exist.</li>
<li><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, creates dataset from <code class="docutils literal notranslate"><span class="pre">training.pt</span></code>,
otherwise from <code class="docutils literal notranslate"><span class="pre">test.pt</span></code>.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="fashion-mnist">
<h2><a class="toc-backref" href="#id8">Fashion-MNIST</a><a class="headerlink" href="#fashion-mnist" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.FashionMNIST">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">FashionMNIST</code><span class="sig-paren">(</span><em>root</em>, <em>train=True</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/mnist.html#FashionMNIST"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.FashionMNIST" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://github.com/zalandoresearch/fashion-mnist">Fashion-MNIST</a> Dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory of dataset where <code class="docutils literal notranslate"><span class="pre">processed/training.pt</span></code>
and <code class="docutils literal notranslate"><span class="pre">processed/test.pt</span></code> exist.</li>
<li><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, creates dataset from <code class="docutils literal notranslate"><span class="pre">training.pt</span></code>,
otherwise from <code class="docutils literal notranslate"><span class="pre">test.pt</span></code>.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="emnist">
<h2><a class="toc-backref" href="#id9">EMNIST</a><a class="headerlink" href="#emnist" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.EMNIST">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">EMNIST</code><span class="sig-paren">(</span><em>root</em>, <em>split</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/mnist.html#EMNIST"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.EMNIST" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://www.nist.gov/itl/iad/image-group/emnist-dataset/">EMNIST</a> Dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory of dataset where <code class="docutils literal notranslate"><span class="pre">processed/training.pt</span></code>
and <code class="docutils literal notranslate"><span class="pre">processed/test.pt</span></code> exist.</li>
<li><strong>split</strong> (<em>string</em>) – The dataset has 6 different splits: <code class="docutils literal notranslate"><span class="pre">byclass</span></code>, <code class="docutils literal notranslate"><span class="pre">bymerge</span></code>,
<code class="docutils literal notranslate"><span class="pre">balanced</span></code>, <code class="docutils literal notranslate"><span class="pre">letters</span></code>, <code class="docutils literal notranslate"><span class="pre">digits</span></code> and <code class="docutils literal notranslate"><span class="pre">mnist</span></code>. This argument specifies
which one to use.</li>
<li><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, creates dataset from <code class="docutils literal notranslate"><span class="pre">training.pt</span></code>,
otherwise from <code class="docutils literal notranslate"><span class="pre">test.pt</span></code>.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="coco">
<h2><a class="toc-backref" href="#id10">COCO</a><a class="headerlink" href="#coco" title="Permalink to this headline">¶</a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">These require the <a class="reference external" href="https://github.com/pdollar/coco/tree/master/PythonAPI">COCO API to be installed</a></p>
</div>
<div class="section" id="captions">
<h3><a class="toc-backref" href="#id11">Captions</a><a class="headerlink" href="#captions" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torchvision.datasets.CocoCaptions">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">CocoCaptions</code><span class="sig-paren">(</span><em>root</em>, <em>annFile</em>, <em>transform=None</em>, <em>target_transform=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/coco.html#CocoCaptions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CocoCaptions" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://mscoco.org/dataset/#captions-challenge2015">MS Coco Captions</a> Dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory where images are downloaded to.</li>
<li><strong>annFile</strong> (<em>string</em>) – Path to json annotation file.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.ToTensor</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.datasets</span> <span class="k">as</span> <span class="nn">dset</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
<span class="n">cap</span> <span class="o">=</span> <span class="n">dset</span><span class="o">.</span><span class="n">CocoCaptions</span><span class="p">(</span><span class="n">root</span> <span class="o">=</span> <span class="s1">'dir where images are'</span><span class="p">,</span>
<span class="n">annFile</span> <span class="o">=</span> <span class="s1">'json annotation file'</span><span class="p">,</span>
<span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Number of samples: '</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">cap</span><span class="p">))</span>
<span class="n">img</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">cap</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># load 4th sample</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Image Size: "</span><span class="p">,</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
</pre></div>
</div>
<p>Output:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Number</span> <span class="n">of</span> <span class="n">samples</span><span class="p">:</span> <span class="mi">82783</span>
<span class="n">Image</span> <span class="n">Size</span><span class="p">:</span> <span class="p">(</span><span class="mi">3</span><span class="n">L</span><span class="p">,</span> <span class="mi">427</span><span class="n">L</span><span class="p">,</span> <span class="mi">640</span><span class="n">L</span><span class="p">)</span>
<span class="p">[</span><span class="sa">u</span><span class="s1">'A plane emitting smoke stream flying over a mountain.'</span><span class="p">,</span>
<span class="sa">u</span><span class="s1">'A plane darts across a bright blue sky behind a mountain covered in snow'</span><span class="p">,</span>
<span class="sa">u</span><span class="s1">'A plane leaves a contrail above the snowy mountain top.'</span><span class="p">,</span>
<span class="sa">u</span><span class="s1">'A mountain that has a plane flying overheard in the distance.'</span><span class="p">,</span>
<span class="sa">u</span><span class="s1">'A mountain view with a plume of smoke in the background'</span><span class="p">]</span>
</pre></div>
</div>
<dl class="method">
<dt id="torchvision.datasets.CocoCaptions.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/coco.html#CocoCaptions.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CocoCaptions.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Tuple (image, target). target is a list of captions for the image.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="detection">
<h3><a class="toc-backref" href="#id12">Detection</a><a class="headerlink" href="#detection" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torchvision.datasets.CocoDetection">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">CocoDetection</code><span class="sig-paren">(</span><em>root</em>, <em>annFile</em>, <em>transform=None</em>, <em>target_transform=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/coco.html#CocoDetection"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CocoDetection" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://mscoco.org/dataset/#detections-challenge2016">MS Coco Detection</a> Dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory where images are downloaded to.</li>
<li><strong>annFile</strong> (<em>string</em>) – Path to json annotation file.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.ToTensor</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.CocoDetection.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/coco.html#CocoDetection.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CocoDetection.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Tuple (image, target). target is the object returned by <code class="docutils literal notranslate"><span class="pre">coco.loadAnns</span></code>.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
</div>
<div class="section" id="lsun">
<h2><a class="toc-backref" href="#id13">LSUN</a><a class="headerlink" href="#lsun" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.LSUN">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">LSUN</code><span class="sig-paren">(</span><em>root</em>, <em>classes='train'</em>, <em>transform=None</em>, <em>target_transform=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/lsun.html#LSUN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.LSUN" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://lsun.cs.princeton.edu">LSUN</a> dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory for the database files.</li>
<li><strong>classes</strong> (<em>string</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – One of {‘train’, ‘val’, ‘test’} or a list of
categories to load. e,g. [‘bedroom_train’, ‘church_train’].</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.LSUN.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/lsun.html#LSUN.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.LSUN.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Tuple (image, target) where target is the index of the target category.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="imagefolder">
<h2><a class="toc-backref" href="#id14">ImageFolder</a><a class="headerlink" href="#imagefolder" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.ImageFolder">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">ImageFolder</code><span class="sig-paren">(</span><em>root</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>loader=<function default_loader></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/folder.html#ImageFolder"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.ImageFolder" title="Permalink to this definition">¶</a></dt>
<dd><p>A generic data loader where the images are arranged in this way:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">root</span><span class="o">/</span><span class="n">dog</span><span class="o">/</span><span class="n">xxx</span><span class="o">.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">dog</span><span class="o">/</span><span class="n">xxy</span><span class="o">.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">dog</span><span class="o">/</span><span class="n">xxz</span><span class="o">.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">cat</span><span class="o">/</span><span class="mf">123.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">cat</span><span class="o">/</span><span class="n">nsdf3</span><span class="o">.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">cat</span><span class="o">/</span><span class="n">asd932_</span><span class="o">.</span><span class="n">png</span>
</pre></div>
</div>
<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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory path.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
<li><strong>loader</strong> – A function to load an image given its path.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.ImageFolder.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.datasets.ImageFolder.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(sample, target) where target is class_index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="datasetfolder">
<h2><a class="toc-backref" href="#id15">DatasetFolder</a><a class="headerlink" href="#datasetfolder" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.DatasetFolder">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">DatasetFolder</code><span class="sig-paren">(</span><em>root</em>, <em>loader</em>, <em>extensions</em>, <em>transform=None</em>, <em>target_transform=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/folder.html#DatasetFolder"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.DatasetFolder" title="Permalink to this definition">¶</a></dt>
<dd><p>A generic data loader where the samples are arranged in this way:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">root</span><span class="o">/</span><span class="n">class_x</span><span class="o">/</span><span class="n">xxx</span><span class="o">.</span><span class="n">ext</span>
<span class="n">root</span><span class="o">/</span><span class="n">class_x</span><span class="o">/</span><span class="n">xxy</span><span class="o">.</span><span class="n">ext</span>
<span class="n">root</span><span class="o">/</span><span class="n">class_x</span><span class="o">/</span><span class="n">xxz</span><span class="o">.</span><span class="n">ext</span>
<span class="n">root</span><span class="o">/</span><span class="n">class_y</span><span class="o">/</span><span class="mf">123.</span><span class="n">ext</span>
<span class="n">root</span><span class="o">/</span><span class="n">class_y</span><span class="o">/</span><span class="n">nsdf3</span><span class="o">.</span><span class="n">ext</span>
<span class="n">root</span><span class="o">/</span><span class="n">class_y</span><span class="o">/</span><span class="n">asd932_</span><span class="o">.</span><span class="n">ext</span>
</pre></div>
</div>
<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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory path.</li>
<li><strong>loader</strong> (<em>callable</em>) – A function to load a sample given its path.</li>
<li><strong>extensions</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><em>string</em><em>]</em>) – A list of allowed extensions.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in
a sample and returns a transformed version.
E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code> for images.</li>
<li><strong>target_transform</strong> – A function/transform that takes
in the target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.DatasetFolder.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/folder.html#DatasetFolder.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.DatasetFolder.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(sample, target) where target is class_index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="imagenet-12">
<h2><a class="toc-backref" href="#id16">Imagenet-12</a><a class="headerlink" href="#imagenet-12" title="Permalink to this headline">¶</a></h2>
<p>This should simply be implemented with an <code class="docutils literal notranslate"><span class="pre">ImageFolder</span></code> dataset.
The data is preprocessed <a class="reference external" href="https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset">as described
here</a></p>
<p><a class="reference external" href="https://github.com/pytorch/examples/blob/e0d33a69bec3eb4096c265451dbb85975eb961ea/imagenet/main.py#L113-L126">Here is an
example</a>.</p>
</div>
<div class="section" id="cifar">
<h2><a class="toc-backref" href="#id17">CIFAR</a><a class="headerlink" href="#cifar" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.CIFAR10">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">CIFAR10</code><span class="sig-paren">(</span><em>root</em>, <em>train=True</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/cifar.html#CIFAR10"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CIFAR10" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR10</a> Dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory of dataset where directory
<code class="docutils literal notranslate"><span class="pre">cifar-10-batches-py</span></code> exists or will be saved to if download is set to True.</li>
<li><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, creates dataset from training set, otherwise
creates from test set.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.CIFAR10.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/cifar.html#CIFAR10.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CIFAR10.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(image, target) where target is index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.datasets.CIFAR100">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">CIFAR100</code><span class="sig-paren">(</span><em>root</em>, <em>train=True</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/cifar.html#CIFAR100"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CIFAR100" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR100</a> Dataset.</p>
<p>This is a subclass of the <cite>CIFAR10</cite> Dataset.</p>
</dd></dl>
</div>
<div class="section" id="stl10">
<h2><a class="toc-backref" href="#id18">STL10</a><a class="headerlink" href="#stl10" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.STL10">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">STL10</code><span class="sig-paren">(</span><em>root</em>, <em>split='train'</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/stl10.html#STL10"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.STL10" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://cs.stanford.edu/~acoates/stl10/">STL10</a> Dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory of dataset where directory
<code class="docutils literal notranslate"><span class="pre">stl10_binary</span></code> exists.</li>
<li><strong>split</strong> (<em>string</em>) – One of {‘train’, ‘test’, ‘unlabeled’, ‘train+unlabeled’}.
Accordingly dataset is selected.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.STL10.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/stl10.html#STL10.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.STL10.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(image, target) where target is index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="svhn">
<h2><a class="toc-backref" href="#id19">SVHN</a><a class="headerlink" href="#svhn" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.SVHN">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">SVHN</code><span class="sig-paren">(</span><em>root</em>, <em>split='train'</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/svhn.html#SVHN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.SVHN" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://ufldl.stanford.edu/housenumbers/">SVHN</a> Dataset.
Note: The SVHN dataset assigns the label <cite>10</cite> to the digit <cite>0</cite>. However, in this Dataset,
we assign the label <cite>0</cite> to the digit <cite>0</cite> to be compatible with PyTorch loss functions which
expect the class labels to be in the range <cite>[0, C-1]</cite></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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory of dataset where directory
<code class="docutils literal notranslate"><span class="pre">SVHN</span></code> exists.</li>
<li><strong>split</strong> (<em>string</em>) – One of {‘train’, ‘test’, ‘extra’}.
Accordingly dataset is selected. ‘extra’ is Extra training set.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal notranslate"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.SVHN.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/svhn.html#SVHN.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.SVHN.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(image, target) where target is index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="phototour">
<h2><a class="toc-backref" href="#id20">PhotoTour</a><a class="headerlink" href="#phototour" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.PhotoTour">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">PhotoTour</code><span class="sig-paren">(</span><em>root</em>, <em>name</em>, <em>train=True</em>, <em>transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/phototour.html#PhotoTour"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.PhotoTour" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://phototour.cs.washington.edu/patches/default.htm">Learning Local Image Descriptors Data</a> Dataset.</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"><ul class="first last simple">
<li><strong>root</strong> (<em>string</em>) – Root directory where images are.</li>
<li><strong>name</strong> (<em>string</em>) – Name of the dataset to load.</li>
<li><strong>transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.PhotoTour.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/phototour.html#PhotoTour.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.PhotoTour.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><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>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(data1, data2, matches)</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="sbu">
<h2><a class="toc-backref" href="#id21">SBU</a><a class="headerlink" href="#sbu" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="flickr">
<h2><a class="toc-backref" href="#id22">Flickr</a><a class="headerlink" href="#flickr" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="voc">
<h2><a class="toc-backref" href="#id23">VOC</a><a class="headerlink" href="#voc" title="Permalink to this headline">¶</a></h2>
</div>
</div>
</article>
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<ul>
<li><a class="reference internal" href="#">torchvision.datasets</a><ul>
<li><a class="reference internal" href="#mnist">MNIST</a></li>
<li><a class="reference internal" href="#fashion-mnist">Fashion-MNIST</a></li>
<li><a class="reference internal" href="#emnist">EMNIST</a></li>
<li><a class="reference internal" href="#coco">COCO</a><ul>
<li><a class="reference internal" href="#captions">Captions</a></li>
<li><a class="reference internal" href="#detection">Detection</a></li>
</ul>
</li>
<li><a class="reference internal" href="#lsun">LSUN</a></li>
<li><a class="reference internal" href="#imagefolder">ImageFolder</a></li>
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