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| <div class="section" id="classify-images-with-a-pretrained-model"> |
| <span id="classify-images-with-a-pretrained-model"></span><h1>Classify Images with a PreTrained Model<a class="headerlink" href="#classify-images-with-a-pretrained-model" title="Permalink to this headline">¶</a></h1> |
| <p>MXNet is a flexible and efficient deep learning framework. One of the interesting things that a deep learning |
| algorithm can do is classify real world images.</p> |
| <p>In this tutorial, we show how to use a pre-trained Inception-BatchNorm network to predict the class of an |
| image. For information about the network architecture, see [1].</p> |
| <p>The pre-trained Inception-BatchNorm network is able to be downloaded from <a class="reference external" href="http://data.mxnet.io/mxnet/data/Inception.zip">this link</a> |
| This model gives the recent state-of-art prediction accuracy on image net dataset.</p> |
| <div class="section" id="load-the-mxnet-package"> |
| <span id="load-the-mxnet-package"></span><h2>Load the MXNet Package<a class="headerlink" href="#load-the-mxnet-package" title="Permalink to this headline">¶</a></h2> |
| <p>To get started, load the mxnet package:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">require</span><span class="p">(</span>mxnet<span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span> |
| <span class="c1">## Loading required package: methods</span> |
| </pre></div> |
| </div> |
| <p>Now load the imager package to load and preprocess the images in R:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kn">require</span><span class="p">(</span>imager<span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: imager</span> |
| <span class="c1">## Loading required package: plyr</span> |
| <span class="c1">## Loading required package: magrittr</span> |
| <span class="c1">## Loading required package: stringr</span> |
| <span class="c1">## Loading required package: png</span> |
| <span class="c1">## Loading required package: jpeg</span> |
| <span class="c1">##</span> |
| <span class="c1">## Attaching package: 'imager'</span> |
| <span class="c1">##</span> |
| <span class="c1">## The following object is masked from 'package:magrittr':</span> |
| <span class="c1">##</span> |
| <span class="c1">## add</span> |
| <span class="c1">##</span> |
| <span class="c1">## The following object is masked from 'package:plyr':</span> |
| <span class="c1">##</span> |
| <span class="c1">## liply</span> |
| <span class="c1">##</span> |
| <span class="c1">## The following objects are masked from 'package:stats':</span> |
| <span class="c1">##</span> |
| <span class="c1">## convolve, spectrum</span> |
| <span class="c1">##</span> |
| <span class="c1">## The following object is masked from 'package:graphics':</span> |
| <span class="c1">##</span> |
| <span class="c1">## frame</span> |
| <span class="c1">##</span> |
| <span class="c1">## The following object is masked from 'package:base':</span> |
| <span class="c1">##</span> |
| <span class="c1">## save.image</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="load-the-pretrained-model"> |
| <span id="load-the-pretrained-model"></span><h2>Load the PreTrained Model<a class="headerlink" href="#load-the-pretrained-model" title="Permalink to this headline">¶</a></h2> |
| <p>Make sure you unzip the pre-trained model in the current folder. Use the model |
| loading function to load the model into R:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> model <span class="o">=</span> mx.model.load<span class="p">(</span><span class="s">"Inception/Inception_BN"</span><span class="p">,</span> iteration<span class="o">=</span><span class="m">39</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Load in the mean image, which is used for preprocessing using:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> mean.img <span class="o">=</span> <span class="kp">as.array</span><span class="p">(</span>mx.nd.load<span class="p">(</span><span class="s">"Inception/mean_224.nd"</span><span class="p">)[[</span><span class="s">"mean_img"</span><span class="p">]])</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="load-and-preprocess-the-image"> |
| <span id="load-and-preprocess-the-image"></span><h2>Load and Preprocess the Image<a class="headerlink" href="#load-and-preprocess-the-image" title="Permalink to this headline">¶</a></h2> |
| <p>Now, we are ready to classify a real image. In this example, we simply take the parrots image |
| from the imager package. You can use another image, if you prefer.</p> |
| <p>Load and plot the image:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> im <span class="o"><-</span> load.image<span class="p">(</span><span class="kp">system.file</span><span class="p">(</span><span class="s">"extdata/parrots.png"</span><span class="p">,</span> package<span class="o">=</span><span class="s">"imager"</span><span class="p">))</span> |
| plot<span class="p">(</span>im<span class="p">)</span> |
| </pre></div> |
| </div> |
| <p><img alt="plot of chunk unnamed-chunk-5" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/classifyRealImageWithPretrainedModel-unnamed-chunk-5-1.png"/></p> |
| <p>Before feeding the image to the deep network, we need to perform some preprocessing |
| to make the image meet the deep network input requirements. Preprocessing |
| includes cropping and subtracting the mean. |
| Because MXNet is deeply integrated with R, we can do all the processing in an R function:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> preproc.image <span class="o"><-</span> <span class="kr">function</span><span class="p">(</span>im<span class="p">,</span> mean.image<span class="p">)</span> <span class="p">{</span> |
| <span class="c1"># crop the image</span> |
| shape <span class="o"><-</span> <span class="kp">dim</span><span class="p">(</span>im<span class="p">)</span> |
| short.edge <span class="o"><-</span> <span class="kp">min</span><span class="p">(</span>shape<span class="p">[</span><span class="m">1</span><span class="o">:</span><span class="m">2</span><span class="p">])</span> |
| xx <span class="o"><-</span> <span class="kp">floor</span><span class="p">((</span>shape<span class="p">[</span><span class="m">1</span><span class="p">]</span> <span class="o">-</span> short.edge<span class="p">)</span> <span class="o">/</span> <span class="m">2</span><span class="p">)</span> |
| yy <span class="o"><-</span> <span class="kp">floor</span><span class="p">((</span>shape<span class="p">[</span><span class="m">2</span><span class="p">]</span> <span class="o">-</span> short.edge<span class="p">)</span> <span class="o">/</span> <span class="m">2</span><span class="p">)</span> |
| cropped <span class="o"><-</span> crop.borders<span class="p">(</span>im<span class="p">,</span> xx<span class="p">,</span> yy<span class="p">)</span> |
| <span class="c1"># resize to 224 x 224, needed by input of the model.</span> |
| resized <span class="o"><-</span> resize<span class="p">(</span>cropped<span class="p">,</span> <span class="m">224</span><span class="p">,</span> <span class="m">224</span><span class="p">)</span> |
| <span class="c1"># convert to array (x, y, channel)</span> |
| arr <span class="o"><-</span> <span class="kp">as.array</span><span class="p">(</span>resized<span class="p">)</span> <span class="o">*</span> <span class="m">255</span> |
| <span class="kp">dim</span><span class="p">(</span>arr<span class="p">)</span> <span class="o"><-</span> <span class="kt">c</span><span class="p">(</span><span class="m">224</span><span class="p">,</span> <span class="m">224</span><span class="p">,</span> <span class="m">3</span><span class="p">)</span> |
| <span class="c1"># subtract the mean</span> |
| normed <span class="o"><-</span> arr <span class="o">-</span> mean.img |
| <span class="c1"># Reshape to format needed by mxnet (width, height, channel, num)</span> |
| <span class="kp">dim</span><span class="p">(</span>normed<span class="p">)</span> <span class="o"><-</span> <span class="kt">c</span><span class="p">(</span><span class="m">224</span><span class="p">,</span> <span class="m">224</span><span class="p">,</span> <span class="m">3</span><span class="p">,</span> <span class="m">1</span><span class="p">)</span> |
| <span class="kr">return</span><span class="p">(</span>normed<span class="p">)</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| <p>Use the defined preprocessing function to get the normalized image:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> normed <span class="o"><-</span> preproc.image<span class="p">(</span>im<span class="p">,</span> mean.img<span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="classify-the-image"> |
| <span id="classify-the-image"></span><h2>Classify the Image<a class="headerlink" href="#classify-the-image" title="Permalink to this headline">¶</a></h2> |
| <p>Now we are ready to classify the image! Use the <code class="docutils literal"><span class="pre">predict</span></code> function |
| to get the probability over classes:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> prob <span class="o"><-</span> predict<span class="p">(</span>model<span class="p">,</span> X<span class="o">=</span>normed<span class="p">)</span> |
| <span class="kp">dim</span><span class="p">(</span>prob<span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] 1000 1</span> |
| </pre></div> |
| </div> |
| <p>As you can see, <code class="docutils literal"><span class="pre">prob</span></code> is a 1 times 1000 array, which gives the probability |
| over the 1000 image classes of the input.</p> |
| <p>Use the <code class="docutils literal"><span class="pre">max.col</span></code> on the transpose of <code class="docutils literal"><span class="pre">prob</span></code> to get the class index:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> max.idx <span class="o"><-</span> <span class="kp">max.col</span><span class="p">(</span><span class="kp">t</span><span class="p">(</span>prob<span class="p">))</span> |
| max.idx |
| </pre></div> |
| </div> |
| <div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] 89</span> |
| </pre></div> |
| </div> |
| <p>The index doesn’t make much sense, so let’s see what it really means. |
| Read the names of the classes from the following file:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> synsets <span class="o"><-</span> <span class="kp">readLines</span><span class="p">(</span><span class="s">"Inception/synset.txt"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Let’s see what the image really is:</p> |
| <div class="highlight-r"><div class="highlight"><pre><span></span> <span class="kp">print</span><span class="p">(</span><span class="kp">paste0</span><span class="p">(</span><span class="s">"Predicted Top-class: "</span><span class="p">,</span> synsets <span class="p">[[</span>max.idx<span class="p">]]))</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python"><div class="highlight"><pre><span></span> <span class="c1">## [1] "Predicted Top-class: n01818515 macaw"</span> |
| </pre></div> |
| </div> |
| <p>It’s a macaw!</p> |
| </div> |
| <div class="section" id="reference"> |
| <span id="reference"></span><h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2> |
| <p>[1] Ioffe, Sergey, and Christian Szegedy. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” arXiv preprint arXiv:1502.03167 (2015).</p> |
| </div> |
| <div class="section" id="next-steps"> |
| <span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2> |
| <div class="toctree-wrapper compound"> |
| <ul> |
| <li class="toctree-l1"><a class="reference external" href="https://mxnet.incubator.apache.org/tutorials/r/mnistCompetition.html">Handwritten Digits Classification Competition</a></li> |
| <li class="toctree-l1"><a class="reference external" href="https://mxnet.incubator.apache.org/tutorials/r/charRnnModel.html">Character Language Model using RNN</a></li> |
| </ul> |
| </div> |
| </div> |
| </div> |
| </div> |
| </div> |
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| <h3><a href="../../index.html">Table Of Contents</a></h3> |
| <ul> |
| <li><a class="reference internal" href="#">Classify Images with a PreTrained Model</a><ul> |
| <li><a class="reference internal" href="#load-the-mxnet-package">Load the MXNet Package</a></li> |
| <li><a class="reference internal" href="#load-the-pretrained-model">Load the PreTrained Model</a></li> |
| <li><a class="reference internal" href="#load-and-preprocess-the-image">Load and Preprocess the Image</a></li> |
| <li><a class="reference internal" href="#classify-the-image">Classify the Image</a></li> |
| <li><a class="reference internal" href="#reference">Reference</a></li> |
| <li><a class="reference internal" href="#next-steps">Next Steps</a></li> |
| </ul> |
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