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 --><div class="section" id="image-classification-using-residual-networks">
 <h1>Image Classification using Residual Networks<a class="headerlink" href="#image-classification-using-residual-networks" title="Permalink to this headline">¶</a></h1>
 <p>In this example, we convert Residual Networks trained on <a class="reference external" href="https://github.com/facebook/fb.resnet.torch">Torch</a> to SINGA for image classification. Tested on [SINGA commit] with the <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz">parameters pretrained by Torch</a></p>
 <div class="section" id="instructions">
 <h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first">Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
   $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
   $ tar xvf resnet-18.tar.gz
 </pre></div>
 </div>
 </li>
 <li><p class="first">Usage</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  $ python serve.py -h
 </pre></div>
 </div>
 </li>
 <li><p class="first">Example</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  # use cpu
   $ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 &amp;
   # use gpu
   $ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 &amp;
 </pre></div>
 </div>
 <p>The parameter files for the following model and depth configuration pairs are provided:</p>
 <ul class="simple">
 <li>resnet (original resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz">18</a>|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-34.tar.gz">34</a>|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-101.tar.gz">101</a>|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-152.tar.gz">152</a></li>
 <li>addbn (resnet with a batch normalization layer after the addition), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz">50</a></li>
 <li>wrn (wide resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz">50</a></li>
 <li>preact (resnet with pre-activation) <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.tar.gz">200</a></li>
 </ul>
 </li>
 <li><p class="first">Submit images for classification</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  $ curl -i -F image=@image1.jpg http://localhost:9999/api
   $ curl -i -F image=@image2.jpg http://localhost:9999/api
   $ curl -i -F image=@image3.jpg http://localhost:9999/api
 </pre></div>
 </div>
 </li>
 </ul>
 <p>image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.</p>
 </div>
 <div class="section" id="details">
 <h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2>
 <p>The parameter files were extracted from the original <a class="reference external" href="https://github.com/facebook/fb.resnet.torch/tree/master/pretrained">torch files</a> via
 the convert.py program.</p>
 <p>Usage:</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>$ python convert.py -h
 </pre></div>
 </div>
 </div>
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	--><div class="section" id="image-classification-using-residual-networks">
	<h1>Image Classification using Residual Networks<a class="headerlink" href="#image-classification-using-residual-networks" title="Permalink to this headline">¶</a></h1>
	<p>In this example, we convert Residual Networks trained on <a class="reference external" href="https://github.com/facebook/fb.resnet.torch">Torch</a> to SINGA for image classification. Tested on [SINGA commit] with the <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz">parameters pretrained by Torch</a></p>
	<div class="section" id="instructions">
	<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
	<ul>
	<li><p class="first">Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
	$ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
	$ tar xvf resnet-18.tar.gz
	</pre></div>
	</div>
	</li>
	<li><p class="first">Usage</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> $ python serve.py -h
	</pre></div>
	</div>
	</li>
	<li><p class="first">Example</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> # use cpu
	$ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 &
	# use gpu
	$ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 &
	</pre></div>
	</div>
	<p>The parameter files for the following model and depth configuration pairs are provided:</p>
	<ul class="simple">
	<li>resnet (original resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz">18</a>\|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-34.tar.gz">34</a>\|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-101.tar.gz">101</a>\|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-152.tar.gz">152</a></li>
	<li>addbn (resnet with a batch normalization layer after the addition), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz">50</a></li>
	<li>wrn (wide resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz">50</a></li>
	<li>preact (resnet with pre-activation) <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.tar.gz">200</a></li>
	</ul>
	</li>
	<li><p class="first">Submit images for classification</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> $ curl -i -F image=@image1.jpg http://localhost:9999/api
	$ curl -i -F image=@image2.jpg http://localhost:9999/api
	$ curl -i -F image=@image3.jpg http://localhost:9999/api
	</pre></div>
	</div>
	</li>
	</ul>
	<p>image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.</p>
	</div>
	<div class="section" id="details">
	<h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2>
	<p>The parameter files were extracted from the original <a class="reference external" href="https://github.com/facebook/fb.resnet.torch/tree/master/pretrained">torch files</a> via
	the convert.py program.</p>
	<p>Usage:</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span>$ python convert.py -h
	</pre></div>
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