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<div class="section" id="torchreid-models">
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<span id="id1"></span><h1>torchreid.models<a class="headerlink" href="#torchreid-models" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="module-torchreid.models.__init__">
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<span id="interface"></span><h2>Interface<a class="headerlink" href="#module-torchreid.models.__init__" title="Permalink to this headline">¶</a></h2>
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<dl class="function">
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<dt id="torchreid.models.__init__.build_model">
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<code class="descclassname">torchreid.models.__init__.</code><code class="descname">build_model</code><span class="sig-paren">(</span><em>name</em>, <em>num_classes</em>, <em>loss='softmax'</em>, <em>pretrained=True</em>, <em>use_gpu=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/__init__.html#build_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.__init__.build_model" title="Permalink to this definition">¶</a></dt>
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<dd><p>A function wrapper for building a model.</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>name</strong> (<em>str</em>) – model name.</li>
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<li><strong>num_classes</strong> (<em>int</em>) – number of training identities.</li>
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<li><strong>loss</strong> (<em>str</em><em>, </em><em>optional</em>) – loss function to optimize the model. Currently
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supports “softmax” and “triplet”. Default is “softmax”.</li>
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<li><strong>pretrained</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to load ImageNet-pretrained weights.
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Default is True.</li>
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<li><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to use gpu. Default is True.</li>
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</ul>
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</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">nn.Module</p>
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</td>
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</tr>
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</tbody>
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</table>
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<dl class="docutils">
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<dt>Examples::</dt>
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<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">models</span>
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<span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span><span class="s1">'resnet50'</span><span class="p">,</span> <span class="mi">751</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">)</span>
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</pre></div>
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</div>
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</dd>
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</dl>
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</dd></dl>
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<dl class="function">
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<dt id="torchreid.models.__init__.show_avai_models">
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<code class="descclassname">torchreid.models.__init__.</code><code class="descname">show_avai_models</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/__init__.html#show_avai_models"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.__init__.show_avai_models" title="Permalink to this definition">¶</a></dt>
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<dd><p>Displays available models.</p>
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<dl class="docutils">
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||
<dt>Examples::</dt>
|
||
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">models</span>
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<span class="gp">>>> </span><span class="n">models</span><span class="o">.</span><span class="n">show_avai_models</span><span class="p">()</span>
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</pre></div>
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</div>
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</dd>
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</dl>
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</dd></dl>
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</div>
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<div class="section" id="imagenet-classification-models">
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<h2>ImageNet Classification Models<a class="headerlink" href="#imagenet-classification-models" title="Permalink to this headline">¶</a></h2>
|
||
<dl class="class">
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||
<dt id="torchreid.models.resnet.ResNet">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.resnet.</code><code class="descname">ResNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>block</em>, <em>layers</em>, <em>zero_init_residual=False</em>, <em>groups=1</em>, <em>width_per_group=64</em>, <em>replace_stride_with_dilation=None</em>, <em>norm_layer=None</em>, <em>last_stride=2</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/resnet.html#ResNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.resnet.ResNet" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Residual network.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li>He et al. Deep Residual Learning for Image Recognition. CVPR 2016.</li>
|
||
<li>Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.</li>
|
||
</ul>
|
||
</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">resnet18</span></code>: ResNet18.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">resnet34</span></code>: ResNet34.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">resnet50</span></code>: ResNet50.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">resnet101</span></code>: ResNet101.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">resnet152</span></code>: ResNet152.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">resnext50_32x4d</span></code>: ResNeXt50.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">resnext101_32x8d</span></code>: ResNeXt101.</li>
|
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<li><code class="docutils literal notranslate"><span class="pre">resnet50_fc512</span></code>: ResNet50 + FC.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.senet.SENet">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.senet.</code><code class="descname">SENet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>block</em>, <em>layers</em>, <em>groups</em>, <em>reduction</em>, <em>dropout_p=0.2</em>, <em>inplanes=128</em>, <em>input_3x3=True</em>, <em>downsample_kernel_size=3</em>, <em>downsample_padding=1</em>, <em>last_stride=2</em>, <em>fc_dims=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/senet.html#SENet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.senet.SENet" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Squeeze-and-excitation network.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">senet154</span></code>: SENet154.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">se_resnet50</span></code>: ResNet50 + SE.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">se_resnet101</span></code>: ResNet101 + SE.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">se_resnet152</span></code>: ResNet152 + SE.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">se_resnext50_32x4d</span></code>: ResNeXt50 (groups=32, width=4) + SE.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">se_resnext101_32x4d</span></code>: ResNeXt101 (groups=32, width=4) + SE.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">se_resnet50_fc512</span></code>: (ResNet50 + SE) + FC.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.densenet.DenseNet">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.densenet.</code><code class="descname">DenseNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>growth_rate=32</em>, <em>block_config=(6</em>, <em>12</em>, <em>24</em>, <em>16)</em>, <em>num_init_features=64</em>, <em>bn_size=4</em>, <em>drop_rate=0</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/densenet.html#DenseNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.densenet.DenseNet" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Densely connected network.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Huang et al. Densely Connected Convolutional Networks. CVPR 2017.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">densenet121</span></code>: DenseNet121.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">densenet169</span></code>: DenseNet169.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">densenet201</span></code>: DenseNet201.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">densenet161</span></code>: DenseNet161.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">densenet121_fc512</span></code>: DenseNet121 + FC.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.inceptionresnetv2.InceptionResNetV2">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.inceptionresnetv2.</code><code class="descname">InceptionResNetV2</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss='softmax'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/inceptionresnetv2.html#InceptionResNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.inceptionresnetv2.InceptionResNetV2" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Inception-ResNet-V2.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
|
||
Connections on Learning. AAAI 2017.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">inceptionresnetv2</span></code>: Inception-ResNet-V2.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.inceptionv4.InceptionV4">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.inceptionv4.</code><code class="descname">InceptionV4</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/inceptionv4.html#InceptionV4"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.inceptionv4.InceptionV4" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Inception-v4.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
|
||
Connections on Learning. AAAI 2017.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">inceptionv4</span></code>: InceptionV4.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.xception.Xception">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.xception.</code><code class="descname">Xception</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/xception.html#Xception"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.xception.Xception" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Xception.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Chollet. Xception: Deep Learning with Depthwise
|
||
Separable Convolutions. CVPR 2017.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">xception</span></code>: Xception.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</div>
|
||
<div class="section" id="lightweight-models">
|
||
<h2>Lightweight Models<a class="headerlink" href="#lightweight-models" title="Permalink to this headline">¶</a></h2>
|
||
<dl class="class">
|
||
<dt id="torchreid.models.nasnet.NASNetAMobile">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.nasnet.</code><code class="descname">NASNetAMobile</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>stem_filters=32</em>, <em>penultimate_filters=1056</em>, <em>filters_multiplier=2</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/nasnet.html#NASNetAMobile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.nasnet.NASNetAMobile" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Neural Architecture Search (NAS).</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Zoph et al. Learning Transferable Architectures
|
||
for Scalable Image Recognition. CVPR 2018.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">nasnetamobile</span></code>: NASNet-A Mobile.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.mobilenetv2.MobileNetV2">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.mobilenetv2.</code><code class="descname">MobileNetV2</code><span class="sig-paren">(</span><em>num_classes</em>, <em>width_mult=1</em>, <em>loss='softmax'</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mobilenetv2.html#MobileNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mobilenetv2.MobileNetV2" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>MobileNetV2.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Sandler et al. MobileNetV2: Inverted Residuals and
|
||
Linear Bottlenecks. CVPR 2018.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">mobilenetv2_x1_0</span></code>: MobileNetV2 x1.0.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">mobilenetv2_x1_4</span></code>: MobileNetV2 x1.4.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.shufflenet.ShuffleNet">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.shufflenet.</code><code class="descname">ShuffleNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss='softmax'</em>, <em>num_groups=3</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/shufflenet.html#ShuffleNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.shufflenet.ShuffleNet" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>ShuffleNet.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural
|
||
Network for Mobile Devices. CVPR 2018.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">shufflenet</span></code>: ShuffleNet (groups=3).</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.squeezenet.SqueezeNet">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.squeezenet.</code><code class="descname">SqueezeNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>version=1.0</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/squeezenet.html#SqueezeNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.squeezenet.SqueezeNet" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>SqueezeNet.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
|
||
and< 0.5 MB model size. arXiv:1602.07360.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">squeezenet1_0</span></code>: SqueezeNet (version=1.0).</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">squeezenet1_1</span></code>: SqueezeNet (version=1.1).</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">squeezenet1_0_fc512</span></code>: SqueezeNet (version=1.0) + FC.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.shufflenetv2.ShuffleNetV2">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.shufflenetv2.</code><code class="descname">ShuffleNetV2</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>stages_repeats</em>, <em>stages_out_channels</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/shufflenetv2.html#ShuffleNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.shufflenetv2.ShuffleNetV2" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>ShuffleNetV2.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x0_5</span></code>: ShuffleNetV2 x0.5.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x1_0</span></code>: ShuffleNetV2 x1.0.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x1_5</span></code>: ShuffleNetV2 x1.5.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x2_0</span></code>: ShuffleNetV2 x2.0.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</div>
|
||
<div class="section" id="reid-specific-models">
|
||
<h2>ReID-specific Models<a class="headerlink" href="#reid-specific-models" title="Permalink to this headline">¶</a></h2>
|
||
<dl class="class">
|
||
<dt id="torchreid.models.mudeep.MuDeep">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.mudeep.</code><code class="descname">MuDeep</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss='softmax'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mudeep.html#MuDeep"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mudeep.MuDeep" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Multiscale deep neural network.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Qian et al. Multi-scale Deep Learning Architectures
|
||
for Person Re-identification. ICCV 2017.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">mudeep</span></code>: Multiscale deep neural network.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.resnetmid.ResNetMid">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.resnetmid.</code><code class="descname">ResNetMid</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>block</em>, <em>layers</em>, <em>last_stride=2</em>, <em>fc_dims=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/resnetmid.html#ResNetMid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.resnetmid.ResNetMid" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Residual network + mid-level features.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for
|
||
Cross-Domain Instance Matching. arXiv:1711.08106.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">resnet50mid</span></code>: ResNet50 + mid-level feature fusion.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.hacnn.HACNN">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.hacnn.</code><code class="descname">HACNN</code><span class="sig-paren">(</span><em>num_classes, loss='softmax', nchannels=[128, 256, 384], feat_dim=512, learn_region=True, use_gpu=True, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/hacnn.html#HACNN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.hacnn.HACNN" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Harmonious Attention Convolutional Neural Network.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Li et al. Harmonious Attention Network for Person Re-identification. CVPR 2018.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">hacnn</span></code>: HACNN.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.pcb.PCB">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.pcb.</code><code class="descname">PCB</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>block</em>, <em>layers</em>, <em>parts=6</em>, <em>reduced_dim=256</em>, <em>nonlinear='relu'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/pcb.html#PCB"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.pcb.PCB" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Part-based Convolutional Baseline.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Sun et al. Beyond Part Models: Person Retrieval with Refined
|
||
Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">pcb_p4</span></code>: PCB with 4-part strips.</li>
|
||
<li><code class="docutils literal notranslate"><span class="pre">pcb_p6</span></code>: PCB with 6-part strips.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.mlfn.MLFN">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.mlfn.</code><code class="descname">MLFN</code><span class="sig-paren">(</span><em>num_classes, loss='softmax', groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mlfn.html#MLFN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mlfn.MLFN" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Multi-Level Factorisation Net.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd>Chang et al. Multi-Level Factorisation Net for
|
||
Person Re-Identification. CVPR 2018.</dd>
|
||
<dt>Public keys:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li><code class="docutils literal notranslate"><span class="pre">mlfn</span></code>: MLFN (Multi-Level Factorisation Net).</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="class">
|
||
<dt id="torchreid.models.osnet.OSNet">
|
||
<em class="property">class </em><code class="descclassname">torchreid.models.osnet.</code><code class="descname">OSNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>blocks</em>, <em>layers</em>, <em>channels</em>, <em>feature_dim=512</em>, <em>loss='softmax'</em>, <em>IN=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/osnet.html#OSNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.osnet.OSNet" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Omni-Scale Network.</p>
|
||
<dl class="docutils">
|
||
<dt>Reference:</dt>
|
||
<dd><ul class="first last simple">
|
||
<li>Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
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|
||
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|
||
|
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|
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|
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|
||
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