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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">ImageNet</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">Datasets</p>
<ul class="simple">
<li><p><a class="reference internal" href="#mnist" id="id18">MNIST</a></p></li>
<li><p><a class="reference internal" href="#fashion-mnist" id="id19">Fashion-MNIST</a></p></li>
<li><p><a class="reference internal" href="#kmnist" id="id20">KMNIST</a></p></li>
<li><p><a class="reference internal" href="#emnist" id="id21">EMNIST</a></p></li>
<li><p><a class="reference internal" href="#qmnist" id="id22">QMNIST</a></p></li>
<li><p><a class="reference internal" href="#fakedata" id="id23">FakeData</a></p></li>
<li><p><a class="reference internal" href="#coco" id="id24">COCO</a></p>
<ul>
<li><p><a class="reference internal" href="#captions" id="id25">Captions</a></p></li>
<li><p><a class="reference internal" href="#detection" id="id26">Detection</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#lsun" id="id27">LSUN</a></p></li>
<li><p><a class="reference internal" href="#imagefolder" id="id28">ImageFolder</a></p></li>
<li><p><a class="reference internal" href="#datasetfolder" id="id29">DatasetFolder</a></p></li>
<li><p><a class="reference internal" href="#imagenet" id="id30">ImageNet</a></p></li>
<li><p><a class="reference internal" href="#cifar" id="id31">CIFAR</a></p></li>
<li><p><a class="reference internal" href="#stl10" id="id32">STL10</a></p></li>
<li><p><a class="reference internal" href="#svhn" id="id33">SVHN</a></p></li>
<li><p><a class="reference internal" href="#phototour" id="id34">PhotoTour</a></p></li>
<li><p><a class="reference internal" href="#sbu" id="id35">SBU</a></p></li>
<li><p><a class="reference internal" href="#flickr" id="id36">Flickr</a></p></li>
<li><p><a class="reference internal" href="#voc" id="id37">VOC</a></p></li>
<li><p><a class="reference internal" href="#cityscapes" id="id38">Cityscapes</a></p></li>
<li><p><a class="reference internal" href="#sbd" id="id39">SBD</a></p></li>
<li><p><a class="reference internal" href="#usps" id="id40">USPS</a></p></li>
<li><p><a class="reference internal" href="#kinetics-400" id="id41">Kinetics-400</a></p></li>
<li><p><a class="reference internal" href="#hmdb51" id="id42">HMDB51</a></p></li>
<li><p><a class="reference internal" href="#ucf101" id="id43">UCF101</a></p></li>
<li><p><a class="reference internal" href="#celeba" id="id44">CelebA</a></p></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="#id18">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">MNIST</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">train=True</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory of dataset where <code class="docutils literal notranslate"><span class="pre">MNIST/processed/training.pt</span></code>
and <code class="docutils literal notranslate"><span class="pre">MNIST/processed/test.pt</span></code> exist.</p></li>
<li><p><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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>.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="fashion-mnist">
<h2><a class="toc-backref" href="#id19">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">FashionMNIST</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">train=True</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory of dataset where <code class="docutils literal notranslate"><span class="pre">Fashion-MNIST/processed/training.pt</span></code>
and <code class="docutils literal notranslate"><span class="pre">Fashion-MNIST/processed/test.pt</span></code> exist.</p></li>
<li><p><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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>.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="kmnist">
<h2><a class="toc-backref" href="#id20">KMNIST</a><a class="headerlink" href="#kmnist" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.KMNIST">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">KMNIST</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">train=True</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/mnist.html#KMNIST"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.KMNIST" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://github.com/rois-codh/kmnist">Kuzushiji-MNIST</a> Dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory of dataset where <code class="docutils literal notranslate"><span class="pre">KMNIST/processed/training.pt</span></code>
and <code class="docutils literal notranslate"><span class="pre">KMNIST/processed/test.pt</span></code> exist.</p></li>
<li><p><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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>.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="emnist">
<h2><a class="toc-backref" href="#id21">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">EMNIST</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">split</em>, <em class="sig-param">**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.westernsydney.edu.au/bens/home/reproducible_research/emnist">EMNIST</a> Dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory of dataset where <code class="docutils literal notranslate"><span class="pre">EMNIST/processed/training.pt</span></code>
and <code class="docutils literal notranslate"><span class="pre">EMNIST/processed/test.pt</span></code> exist.</p></li>
<li><p><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.</p></li>
<li><p><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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>.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="qmnist">
<h2><a class="toc-backref" href="#id22">QMNIST</a><a class="headerlink" href="#qmnist" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.QMNIST">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">QMNIST</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">what=None</em>, <em class="sig-param">compat=True</em>, <em class="sig-param">train=True</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/mnist.html#QMNIST"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.QMNIST" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://github.com/facebookresearch/qmnist">QMNIST</a> Dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory of dataset whose <a href="#id5"><span class="problematic" id="id6">``</span></a>processed’’
subdir contains torch binary files with the datasets.</p></li>
<li><p><strong>what</strong> (<em>string</em><em>,</em><em>optional</em>) – Can be ‘train’, ‘test’, ‘test10k’,
‘test50k’, or ‘nist’ for respectively the mnist compatible
training set, the 60k qmnist testing set, the 10k qmnist
examples that match the mnist testing set, the 50k
remaining qmnist testing examples, or all the nist
digits. The default is to select ‘train’ or ‘test’
according to the compatibility argument ‘train’.</p></li>
<li><p><strong>compat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>,</em><em>optional</em>) – A boolean that says whether the target
for each example is class number (for compatibility with
the MNIST dataloader) or a torch vector containing the
full qmnist information. Default=True.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform
that takes in the target and transforms it.</p></li>
<li><p><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>,</em><em>optional</em><em>,</em><em>compatibility</em>) – When argument ‘what’ is
not specified, this boolean decides whether to load the
training set ot the testing set. Default: True.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="fakedata">
<h2><a class="toc-backref" href="#id23">FakeData</a><a class="headerlink" href="#fakedata" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.FakeData">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">FakeData</code><span class="sig-paren">(</span><em class="sig-param">size=1000</em>, <em class="sig-param">image_size=(3</em>, <em class="sig-param">224</em>, <em class="sig-param">224)</em>, <em class="sig-param">num_classes=10</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">random_offset=0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/fakedata.html#FakeData"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.FakeData" title="Permalink to this definition">¶</a></dt>
<dd><p>A fake dataset that returns randomly generated images and returns them as PIL images</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em>, </em><em>optional</em>) – Size of the dataset. Default: 1000 images</p></li>
<li><p><strong>image_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><em>tuple</em></a><em>, </em><em>optional</em>) – Size if the returned images. Default: (3, 224, 224)</p></li>
<li><p><strong>num_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em>, </em><em>optional</em>) – Number of classes in the datset. Default: 10</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
<li><p><strong>random_offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Offsets the index-based random seed used to
generate each image. Default: 0</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="coco">
<h2><a class="toc-backref" href="#id24">COCO</a><a class="headerlink" href="#coco" title="Permalink to this headline">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>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="#id25">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">CocoCaptions</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">annFile</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">transforms=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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory where images are downloaded to.</p></li>
<li><p><strong>annFile</strong> (<em>string</em>) – Path to json annotation file.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
<li><p><strong>transforms</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes input sample and its target as entry
and returns a transformed version.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-python 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="sig-name descname">__getitem__</code><span class="sig-paren">(</span><em class="sig-param">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><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Tuple (image, target). target is a list of captions for the image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="detection">
<h3><a class="toc-backref" href="#id26">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">CocoDetection</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">annFile</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">transforms=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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory where images are downloaded to.</p></li>
<li><p><strong>annFile</strong> (<em>string</em>) – Path to json annotation file.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
<li><p><strong>transforms</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes input sample and its target as entry
and returns a transformed version.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.datasets.CocoDetection.__getitem__">
<code class="sig-name descname">__getitem__</code><span class="sig-paren">(</span><em class="sig-param">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><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Tuple (image, target). target is the object returned by <code class="docutils literal notranslate"><span class="pre">coco.loadAnns</span></code>.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
</div>
<div class="section" id="lsun">
<h2><a class="toc-backref" href="#id27">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">LSUN</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">classes='train'</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">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="https://www.yf.io/p/lsun">LSUN</a> dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory for the database files.</p></li>
<li><p><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 v3.8)"><em>list</em></a>) – One of {‘train’, ‘val’, ‘test’} or a list of
categories to load. e,g. [‘bedroom_train’, ‘church_outdoor_train’].</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.datasets.LSUN.__getitem__">
<code class="sig-name descname">__getitem__</code><span class="sig-paren">(</span><em class="sig-param">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><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Tuple (image, target) where target is the index of the target category.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="imagefolder">
<h2><a class="toc-backref" href="#id28">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">ImageFolder</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">loader=<function default_loader></em>, <em class="sig-param">is_valid_file=None</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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory path.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
<li><p><strong>loader</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function to load an image given its path.</p></li>
<li><p><strong>is_valid_file</strong> – A function that takes path of an Image file
and check if the file is a valid file (used to check of corrupt files)</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.datasets.ImageFolder.__getitem__">
<code class="sig-name descname">__getitem__</code><span class="sig-paren">(</span><em class="sig-param">index</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.datasets.ImageFolder.__getitem__" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>(sample, target) where target is class_index of the target class.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="datasetfolder">
<h2><a class="toc-backref" href="#id29">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">DatasetFolder</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">loader</em>, <em class="sig-param">extensions=None</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">is_valid_file=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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory path.</p></li>
<li><p><strong>loader</strong> (<em>callable</em>) – A function to load a sample given its path.</p></li>
<li><p><strong>extensions</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><em>tuple</em></a><em>[</em><em>string</em><em>]</em>) – A list of allowed extensions.
both extensions and is_valid_file should not be passed.</p></li>
<li><p><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.</p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes
in the target and transforms it.</p></li>
<li><p><strong>is_valid_file</strong> – A function that takes path of a file
and check if the file is a valid file (used to check of corrupt files)
both extensions and is_valid_file should not be passed.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.datasets.DatasetFolder.__getitem__">
<code class="sig-name descname">__getitem__</code><span class="sig-paren">(</span><em class="sig-param">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><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>(sample, target) where target is class_index of the target class.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="imagenet">
<h2><a class="toc-backref" href="#id30">ImageNet</a><a class="headerlink" href="#imagenet" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.ImageNet">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">ImageNet</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">split='train'</em>, <em class="sig-param">download=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/imagenet.html#ImageNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.ImageNet" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://image-net.org/">ImageNet</a> 2012 Classification Dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory of the ImageNet Dataset.</p></li>
<li><p><strong>split</strong> (<em>string</em><em>, </em><em>optional</em>) – The dataset split, supports <code class="docutils literal notranslate"><span class="pre">train</span></code>, or <code class="docutils literal notranslate"><span class="pre">val</span></code>.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
<li><p><strong>loader</strong> – A function to load an image given its path.</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="cifar">
<h2><a class="toc-backref" href="#id31">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">CIFAR10</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">train=True</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><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.</p></li>
<li><p><strong>train</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, creates dataset from training set, otherwise
creates from test set.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.datasets.CIFAR10.__getitem__">
<code class="sig-name descname">__getitem__</code><span class="sig-paren">(</span><em class="sig-param">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><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>(image, target) where target is index of the target class.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.datasets.CIFAR100">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">CIFAR100</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">train=True</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">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="#id32">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">STL10</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">split='train'</em>, <em class="sig-param">folds=None</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><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.</p></li>
<li><p><strong>split</strong> (<em>string</em>) – One of {‘train’, ‘test’, ‘unlabeled’, ‘train+unlabeled’}.
Accordingly dataset is selected.</p></li>
<li><p><strong>folds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em>, </em><em>optional</em>) – <p>One of {0-9} or None.
For training, loads one of the 10 pre-defined folds of 1k samples for the</p>
<blockquote>
<div><p>standard evaluation procedure. If no value is passed, loads the 5k samples.</p>
</div></blockquote>
</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.datasets.STL10.__getitem__">
<code class="sig-name descname">__getitem__</code><span class="sig-paren">(</span><em class="sig-param">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><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>(image, target) where target is index of the target class.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="svhn">
<h2><a class="toc-backref" href="#id33">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">SVHN</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">split='train'</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">target_transform=None</em>, <em class="sig-param">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>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This class needs <a class="reference external" href="https://docs.scipy.org/doc/">scipy</a> to load data from <cite>.mat</cite> format.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory of dataset where directory
<code class="docutils literal notranslate"><span class="pre">SVHN</span></code> exists.</p></li>
<li><p><strong>split</strong> (<em>string</em>) – One of {‘train’, ‘test’, ‘extra’}.
Accordingly dataset is selected. ‘extra’ is Extra training set.</p></li>
<li><p><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></p></li>
<li><p><strong>target_transform</strong> (<em>callable</em><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.datasets.SVHN.__getitem__">
<code class="sig-name descname">__getitem__</code><span class="sig-paren">(</span><em class="sig-param">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><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – Index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>(image, target) where target is index of the target class.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="phototour">
<h2><a class="toc-backref" href="#id34">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="sig-prename descclassname">torchvision.datasets.</code><code class="sig-name descname">PhotoTour</code><span class="sig-paren">(</span><em class="sig-param">root</em>, <em class="sig-param">name</em>, <em class="sig-param">train=True</em>, <em class="sig-param">transform=None</em>, <em class="sig-param">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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>root</strong> (<em>string</em>) – Root directory where images are.</p></li>
<li><p><strong>name</strong> (<em>string</em>) – Name of the dataset to load.</p></li>
<li><p><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.</p></li>
<li><p><strong>download</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><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.</p></li>
</ul>
</dd>
</dl>