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0.7.0
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<li class="toctree-l1"><a class="reference internal" href="AWESOME_REID.html">Awesome-ReID</a></li>
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<div class="section" id="torchreid">
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<h1>Torchreid<a class="headerlink" href="#torchreid" title="Permalink to this headline">¶</a></h1>
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<p>Torchreid is a library built on <a class="reference external" href="https://pytorch.org/">PyTorch</a> for deep-learning person re-identification.</p>
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<p>It features:</p>
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<ul class="simple">
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<li>multi-GPU training</li>
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<li>support both image- and video-reid</li>
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<li>end-to-end training and evaluation</li>
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<li>incredibly easy preparation of reid datasets</li>
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<li>multi-dataset training</li>
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<li>cross-dataset evaluation</li>
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<li>standard protocol used by most research papers</li>
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<li>highly extensible (easy to add models, datasets, training methods, etc.)</li>
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<li>implementations of state-of-the-art deep reid models</li>
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<li>access to pretrained reid models</li>
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<li>advanced training techniques</li>
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<li>visualization of ranking results</li>
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</ul>
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<p>Documentation: <a class="reference external" href="https://kaiyangzhou.github.io/deep-person-reid/">https://kaiyangzhou.github.io/deep-person-reid/</a>.</p>
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<div class="section" id="news">
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<h2>News<a class="headerlink" href="#news" title="Permalink to this headline">¶</a></h2>
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<p>24-03-2019: <a class="reference external" href="https://kaiyangzhou.github.io/deep-person-reid/">Torchreid documentation</a> is out!</p>
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</div>
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<div class="section" id="installation">
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<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
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<ol class="arabic simple">
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<li>Install PyTorch and torchvision following the <a class="reference external" href="https://pytorch.org/">official instructions</a>.</li>
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<li>Install Cython.</li>
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<li>Clone <code class="docutils literal notranslate"><span class="pre">deep-person-reid</span></code> to your preferred directory.</li>
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</ol>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ git clone https://github.com/KaiyangZhou/deep-person-reid.git
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</pre></div>
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</div>
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<ol class="arabic simple" start="4">
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<li>Install <code class="docutils literal notranslate"><span class="pre">torchreid</span></code> via</li>
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</ol>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> deep-person-reid/
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$ python setup.py install <span class="c1"># or python3</span>
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$ <span class="c1"># If you wanna modify the source code without</span>
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$ <span class="c1"># the need to re-build it, you can do</span>
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$ <span class="c1"># python setup.py develop</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="get-started-30-seconds-to-torchreid">
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<h2>Get started: 30 seconds to Torchreid<a class="headerlink" href="#get-started-30-seconds-to-torchreid" title="Permalink to this headline">¶</a></h2>
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<ol class="arabic simple">
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<li>Import <code class="docutils literal notranslate"><span class="pre">torchreid</span></code></li>
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</ol>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span>
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</pre></div>
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</div>
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<ol class="arabic simple" start="2">
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<li>Load data manager</li>
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</ol>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span>
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<span class="n">root</span><span class="o">=</span><span class="s1">'reid-data'</span><span class="p">,</span>
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<span class="n">sources</span><span class="o">=</span><span class="s1">'market1501'</span><span class="p">,</span>
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<span class="n">height</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
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<span class="n">width</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
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<span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
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<span class="n">market1501_500k</span><span class="o">=</span><span class="bp">False</span>
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<span class="p">)</span>
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</pre></div>
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</div>
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<p>3 Build model, optimizer and lr_scheduler</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span>
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<span class="n">name</span><span class="o">=</span><span class="s1">'resnet50'</span><span class="p">,</span>
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<span class="n">num_classes</span><span class="o">=</span><span class="n">datamanager</span><span class="o">.</span><span class="n">num_train_pids</span><span class="p">,</span>
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<span class="n">loss</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">,</span>
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<span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span>
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<span class="p">)</span>
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<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_optimizer</span><span class="p">(</span>
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<span class="n">model</span><span class="p">,</span>
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<span class="n">optim</span><span class="o">=</span><span class="s1">'adam'</span><span class="p">,</span>
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<span class="n">lr</span><span class="o">=</span><span class="mf">0.0003</span>
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<span class="p">)</span>
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<span class="n">scheduler</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_lr_scheduler</span><span class="p">(</span>
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<span class="n">optimizer</span><span class="p">,</span>
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<span class="n">lr_scheduler</span><span class="o">=</span><span class="s1">'single_step'</span><span class="p">,</span>
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<span class="n">stepsize</span><span class="o">=</span><span class="mi">20</span>
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<span class="p">)</span>
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</pre></div>
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</div>
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<ol class="arabic simple" start="4">
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<li>Build engine</li>
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</ol>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">engine</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">ImageSoftmaxEngine</span><span class="p">(</span>
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<span class="n">datamanager</span><span class="p">,</span>
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<span class="n">model</span><span class="p">,</span>
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<span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span>
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<span class="n">scheduler</span><span class="o">=</span><span class="n">scheduler</span><span class="p">,</span>
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<span class="n">label_smooth</span><span class="o">=</span><span class="bp">True</span>
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<span class="p">)</span>
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</pre></div>
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</div>
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<ol class="arabic simple" start="5">
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<li>Run training and test</li>
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</ol>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">engine</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
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<span class="n">save_dir</span><span class="o">=</span><span class="s1">'log/resnet50'</span><span class="p">,</span>
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<span class="n">max_epoch</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span>
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<span class="n">eval_freq</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
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<span class="n">print_freq</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
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<span class="n">test_only</span><span class="o">=</span><span class="bp">False</span>
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<span class="p">)</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="a-unified-interface">
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<h2>A unified interface<a class="headerlink" href="#a-unified-interface" title="Permalink to this headline">¶</a></h2>
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<p>In “deep-person-reid/scripts/”, we provide a unified interface including a default parser file <code class="docutils literal notranslate"><span class="pre">default_parser.py</span></code> and the main script <code class="docutils literal notranslate"><span class="pre">main.py</span></code>. For example, to train an image reid model on Market1501 using softmax, you can do</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python main.py <span class="se">\</span>
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--root path/to/reid-data <span class="se">\</span>
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--app image <span class="se">\</span>
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--loss softmax <span class="se">\</span>
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--label-smooth <span class="se">\</span>
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-s market1501 <span class="se">\</span>
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-a resnet50 <span class="se">\</span>
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--optim adam <span class="se">\</span>
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--lr <span class="m">0</span>.0003 <span class="se">\</span>
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--max-epoch <span class="m">60</span> <span class="se">\</span>
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--stepsize <span class="m">20</span> <span class="m">40</span> <span class="se">\</span>
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--batch-size <span class="m">32</span> <span class="se">\</span>
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--save-dir log/resnet50-market-softmax <span class="se">\</span>
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--gpu-devices <span class="m">0</span>
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</pre></div>
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</div>
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<p>Please refer to <code class="docutils literal notranslate"><span class="pre">default_parser.py</span></code> and <code class="docutils literal notranslate"><span class="pre">main.py</span></code> for more details.</p>
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</div>
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<div class="section" id="datasets">
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<h2>Datasets<a class="headerlink" href="#datasets" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="image-reid-datasets">
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<h3>Image-reid datasets<a class="headerlink" href="#image-reid-datasets" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf">Market1501</a></li>
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<li><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf">CUHK03</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1701.07717">DukeMTMC-reID</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1711.08565">MSMT17</a></li>
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<li><a class="reference external" href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.7285&rep=rep1&type=pdf">VIPeR</a></li>
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<li><a class="reference external" href="http://www.eecs.qmul.ac.uk/~txiang/publications/LoyXiangGong_cvpr_2009.pdf">GRID</a></li>
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<li><a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf">CUHK01</a></li>
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<li><a class="reference external" href="https://pdfs.semanticscholar.org/f62d/71e701c9fd021610e2076b5e0f5b2c7c86ca.pdf">PRID450S</a></li>
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<li><a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Spindle_Net_Person_CVPR_2017_paper.pdf">SenseReID</a></li>
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<li><a class="reference external" href="http://www.eecs.qmul.ac.uk/~sgg/papers/ZhengGongXiang_BMVC09.pdf">QMUL-iLIDS</a></li>
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<li><a class="reference external" href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">PRID</a></li>
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</ul>
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</div>
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<div class="section" id="video-reid-datasets">
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<h3>Video-reid datasets<a class="headerlink" href="#video-reid-datasets" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><a class="reference external" href="http://www.liangzheng.org/1320.pdf">MARS</a></li>
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<li><a class="reference external" href="https://www.eecs.qmul.ac.uk/~sgg/papers/WangEtAl_ECCV14.pdf">iLIDS-VID</a></li>
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<li><a class="reference external" href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">PRID2011</a></li>
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<li><a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Exploit_the_Unknown_CVPR_2018_paper.pdf">DukeMTMC-VideoReID</a></li>
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</ul>
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</div>
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</div>
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<div class="section" id="models">
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<h2>Models<a class="headerlink" href="#models" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="imagenet-classification-models">
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<h3>ImageNet classification models<a class="headerlink" href="#imagenet-classification-models" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1611.05431">ResNeXt</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1709.01507">SENet</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1602.07261">Inception-ResNet-V2</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1602.07261">Inception-V4</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1610.02357">Xception</a></li>
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</ul>
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</div>
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<div class="section" id="lightweight-models">
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<h3>Lightweight models<a class="headerlink" href="#lightweight-models" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><a class="reference external" href="https://arxiv.org/abs/1707.07012">NASNet</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1801.04381">MobileNetV2</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1707.01083">ShuffleNet</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a></li>
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</ul>
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</div>
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<div class="section" id="reid-specific-models">
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<h3>ReID-specific models<a class="headerlink" href="#reid-specific-models" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><a class="reference external" href="https://arxiv.org/abs/1709.05165">MuDeep</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1711.08106">ResNet-mid</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1802.08122">HACNN</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1711.09349">PCB</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1803.09132">MLFN</a></li>
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</ul>
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</div>
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</div>
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<div class="section" id="losses">
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<h2>Losses<a class="headerlink" href="#losses" title="Permalink to this headline">¶</a></h2>
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<ul class="simple">
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<li><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf">Softmax (cross entropy loss with label smoothing)</a></li>
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<li><a class="reference external" href="https://arxiv.org/abs/1703.07737">Triplet (hard example mining triplet loss)</a></li>
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</ul>
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</div>
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<div class="section" id="citation">
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<h2>Citation<a class="headerlink" href="#citation" title="Permalink to this headline">¶</a></h2>
|
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<p>Please link this project in your paper</p>
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</div>
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<div class="toctree-wrapper compound">
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<div class="section" id="indices-and-tables">
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<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
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<ul class="simple">
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<li><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></li>
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<li><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></li>
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<p>
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© Copyright 2019, Kaiyang Zhou
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