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<h1 id="getting-started">Getting Started</h1>
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<h2 id="install">Install</h2>
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<p>The library can be installed with pip:</p>
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<div class="highlight"><pre><span></span><code>pip install timm
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</code></pre></div>
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<div class="admonition info">
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<p class="admonition-title">Conda Environment</p>
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<p>All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically Python 3.6.x, 3.7.x., 3.8.x.</p>
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<p>Little to no care has been taken to be Python 2.x friendly and will not support it. If you run into any challenges running on Windows, or other OS, I'm definitely open to looking into those issues so long as it's in a reproducible (read Conda) environment.</p>
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<p>PyTorch versions 1.4, 1.5.x, and 1.6 have been tested with this code.</p>
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<p>I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda:
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<div class="highlight"><pre><span></span><code>conda create -n torch-env
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conda activate torch-env
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conda install -c pytorch pytorch torchvision cudatoolkit=10.2
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conda install pyyaml
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</code></pre></div></p>
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</div>
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<h2 id="load-a-pretrained-model">Load a Pretrained Model</h2>
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<p>Pretrained models can be loaded using <code>timm.create_model</code></p>
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<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">timm</span>
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<span class="n">m</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">'mobilenetv3_100'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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<span class="n">m</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
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</code></pre></div>
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<h2 id="list-models-with-pretrained-weights">List Models with Pretrained Weights</h2>
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<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">timm</span>
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<span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
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<span class="n">model_names</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">list_models</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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<span class="n">pprint</span><span class="p">(</span><span class="n">model_names</span><span class="p">)</span>
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<span class="o">>>></span> <span class="p">[</span><span class="s1">'adv_inception_v3'</span><span class="p">,</span>
|
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<span class="s1">'cspdarknet53'</span><span class="p">,</span>
|
|
<span class="s1">'cspresnext50'</span><span class="p">,</span>
|
|
<span class="s1">'densenet121'</span><span class="p">,</span>
|
|
<span class="s1">'densenet161'</span><span class="p">,</span>
|
|
<span class="s1">'densenet169'</span><span class="p">,</span>
|
|
<span class="s1">'densenet201'</span><span class="p">,</span>
|
|
<span class="s1">'densenetblur121d'</span><span class="p">,</span>
|
|
<span class="s1">'dla34'</span><span class="p">,</span>
|
|
<span class="s1">'dla46_c'</span><span class="p">,</span>
|
|
<span class="o">...</span>
|
|
<span class="p">]</span>
|
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</code></pre></div>
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<h2 id="list-model-architectures-by-wildcard">List Model Architectures by Wildcard</h2>
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<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">timm</span>
|
|
<span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
|
|
<span class="n">model_names</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">list_models</span><span class="p">(</span><span class="s1">'*resne*t*'</span><span class="p">)</span>
|
|
<span class="n">pprint</span><span class="p">(</span><span class="n">model_names</span><span class="p">)</span>
|
|
<span class="o">>>></span> <span class="p">[</span><span class="s1">'cspresnet50'</span><span class="p">,</span>
|
|
<span class="s1">'cspresnet50d'</span><span class="p">,</span>
|
|
<span class="s1">'cspresnet50w'</span><span class="p">,</span>
|
|
<span class="s1">'cspresnext50'</span><span class="p">,</span>
|
|
<span class="o">...</span>
|
|
<span class="p">]</span>
|
|
</code></pre></div>
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