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   <div class="section" id="use-parameters-pre-trained-from-caffe-in-singa">
 <span id="use-parameters-pre-trained-from-caffe-in-singa"></span><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">
 <span id="run-this-example"></span><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">
 <span id="obtain-the-caffe-model"></span><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">
 <span id="prepare-test-images"></span><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">
 <span id="predict"></span><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-python"><div class="highlight"><pre>usage: predict.py [-h] model_txt model_bin imgclass
 </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-python"><div class="highlight"><pre>python predict.py vgg16.prototxt vgg16.caffemodel synset_words.txt
 </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>
 </div>
 </div>
 </div>


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	<div class="section" id="use-parameters-pre-trained-from-caffe-in-singa">
	<span id="use-parameters-pre-trained-from-caffe-in-singa"></span><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">
	<span id="run-this-example"></span><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">
	<span id="obtain-the-caffe-model"></span><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">
	<span id="prepare-test-images"></span><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">
	<span id="predict"></span><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-python"><div class="highlight"><pre>usage: predict.py [-h] model_txt model_bin imgclass
	</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-python"><div class="highlight"><pre>python predict.py vgg16.prototxt vgg16.caffemodel synset_words.txt
	</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>
	</div>
	</div>
	</div>


	</div>
	</div>
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