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<h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline"></a></h1>
<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p>
<div class="section" id="run-this-example">
<h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline"></a></h2>
<p>You can run this example by simply executing <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code>
The script does the following work.</p>
<div class="section" id="obtain-the-caffe-model">
<h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li>Download caffe model prototxt and parameter binary file.</li>
<li>Currently we only support the latest caffe format, if your model is in
previous version of caffe, please update it to current format.(This is
supported by caffe)</li>
<li>After updating, we can obtain two files, i.e., the prototxt and parameter
binary file.</li>
</ul>
</div>
<div class="section" id="prepare-test-images">
<h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline"></a></h3>
<p>A few sample images are downloaded into the <code class="docutils literal"><span class="pre">test</span></code> folder.</p>
</div>
<div class="section" id="predict">
<h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline"></a></h3>
<p>The <code class="docutils literal"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">usage</span><span class="p">:</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> <span class="p">[</span><span class="o">-</span><span class="n">h</span><span class="p">]</span> <span class="n">model_txt</span> <span class="n">model_bin</span> <span class="n">imgclass</span>
</pre></div>
</div>
<p>where <code class="docutils literal"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models.
You can start the prediction program by executing the following command:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg16</span><span class="o">.</span><span class="n">prototxt</span> <span class="n">vgg16</span><span class="o">.</span><span class="n">caffemodel</span> <span class="n">synset_words</span><span class="o">.</span><span class="n">txt</span>
</pre></div>
</div>
<p>Then you type in the image path, and the program would output the top-5 labels.</p>
<p>More Caffe models would be tested soon.</p>
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