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| <h1 class="post-title">Classify Images with a PreTrained Model</h1> |
| <h3></h3></header> |
| |
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| |
| <h1 id="classify-images-with-a-pretrained-model">Classify Images with a PreTrained Model</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 href="https://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> |
| |
| <h2 id="load-the-mxnet-package">Load the MXNet Package</h2> |
| <p>To get started, load the mxnet package:</p> |
| |
| <div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">require</span><span class="p">(</span><span class="n">mxnet</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code> ## Loading required package: mxnet |
| ## Loading required package: methods |
| </code></pre></div></div> |
| |
| <p>Now load the imager package to load and preprocess the images in R:</p> |
| |
| <div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">require</span><span class="p">(</span><span class="n">imager</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code> ## Loading required package: imager |
| ## Loading required package: plyr |
| ## Loading required package: magrittr |
| ## Loading required package: stringr |
| ## Loading required package: png |
| ## Loading required package: jpeg |
| ## |
| ## Attaching package: 'imager' |
| ## |
| ## The following object is masked from 'package:magrittr': |
| ## |
| ## add |
| ## |
| ## The following object is masked from 'package:plyr': |
| ## |
| ## liply |
| ## |
| ## The following objects are masked from 'package:stats': |
| ## |
| ## convolve, spectrum |
| ## |
| ## The following object is masked from 'package:graphics': |
| ## |
| ## frame |
| ## |
| ## The following object is masked from 'package:base': |
| ## |
| ## save.image |
| </code></pre></div></div> |
| |
| <h2 id="load-the-pretrained-model">Load the PreTrained Model</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="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.model.load</span><span class="p">(</span><span class="s2">"Inception/Inception_BN"</span><span class="p">,</span><span class="w"> </span><span class="n">iteration</span><span class="o">=</span><span class="m">39</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>Load in the mean image, which is used for preprocessing using:</p> |
| |
| <div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">mean.img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">as.array</span><span class="p">(</span><span class="n">mx.nd.load</span><span class="p">(</span><span class="s2">"Inception/mean_224.nd"</span><span class="p">)[[</span><span class="s2">"mean_img"</span><span class="p">]])</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <h2 id="load-and-preprocess-the-image">Load and Preprocess the Image</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="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">im</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">load.image</span><span class="p">(</span><span class="n">system.file</span><span class="p">(</span><span class="s2">"extdata/parrots.png"</span><span class="p">,</span><span class="w"> </span><span class="n">package</span><span class="o">=</span><span class="s2">"imager"</span><span class="p">))</span><span class="w"> |
| </span><span class="n">plot</span><span class="p">(</span><span class="n">im</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/classifyRealImageWithPretrainedModel-unnamed-chunk-5-1.png" alt="plot of chunk unnamed-chunk-5" /></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="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">preproc.image</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">im</span><span class="p">,</span><span class="w"> </span><span class="n">mean.image</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="c1"># crop the image</span><span class="w"> |
| </span><span class="n">shape</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">dim</span><span class="p">(</span><span class="n">im</span><span class="p">)</span><span class="w"> |
| </span><span class="n">short.edge</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">min</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="m">1</span><span class="o">:</span><span class="m">2</span><span class="p">])</span><span class="w"> |
| </span><span class="n">xx</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">floor</span><span class="p">((</span><span class="n">shape</span><span class="p">[</span><span class="m">1</span><span class="p">]</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">short.edge</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="m">2</span><span class="p">)</span><span class="w"> |
| </span><span class="n">yy</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">floor</span><span class="p">((</span><span class="n">shape</span><span class="p">[</span><span class="m">2</span><span class="p">]</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">short.edge</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="m">2</span><span class="p">)</span><span class="w"> |
| </span><span class="n">cropped</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">crop.borders</span><span class="p">(</span><span class="n">im</span><span class="p">,</span><span class="w"> </span><span class="n">xx</span><span class="p">,</span><span class="w"> </span><span class="n">yy</span><span class="p">)</span><span class="w"> |
| </span><span class="c1"># resize to 224 x 224, needed by input of the model.</span><span class="w"> |
| </span><span class="n">resized</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">resize</span><span class="p">(</span><span class="n">cropped</span><span class="p">,</span><span class="w"> </span><span class="m">224</span><span class="p">,</span><span class="w"> </span><span class="m">224</span><span class="p">)</span><span class="w"> |
| </span><span class="c1"># convert to array (x, y, channel)</span><span class="w"> |
| </span><span class="n">arr</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">as.array</span><span class="p">(</span><span class="n">resized</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="m">255</span><span class="w"> |
| </span><span class="nf">dim</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">224</span><span class="p">,</span><span class="w"> </span><span class="m">224</span><span class="p">,</span><span class="w"> </span><span class="m">3</span><span class="p">)</span><span class="w"> |
| </span><span class="c1"># subtract the mean</span><span class="w"> |
| </span><span class="n">normed</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">arr</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">mean.img</span><span class="w"> |
| </span><span class="c1"># Reshape to format needed by mxnet (width, height, channel, num)</span><span class="w"> |
| </span><span class="nf">dim</span><span class="p">(</span><span class="n">normed</span><span class="p">)</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">224</span><span class="p">,</span><span class="w"> </span><span class="m">224</span><span class="p">,</span><span class="w"> </span><span class="m">3</span><span class="p">,</span><span class="w"> </span><span class="m">1</span><span class="p">)</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="n">normed</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>Use the defined preprocessing function to get the normalized image:</p> |
| |
| <div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">normed</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">preproc.image</span><span class="p">(</span><span class="n">im</span><span class="p">,</span><span class="w"> </span><span class="n">mean.img</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <h2 id="classify-the-image">Classify the Image</h2> |
| <p>Now we are ready to classify the image! Use the <code class="highlighter-rouge">predict</code> function |
| to get the probability over classes:</p> |
| |
| <div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">prob</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">normed</span><span class="p">)</span><span class="w"> |
| </span><span class="nf">dim</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code> ## [1] 1000 1 |
| </code></pre></div></div> |
| |
| <p>As you can see, <code class="highlighter-rouge">prob</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="highlighter-rouge">max.col</code> on the transpose of <code class="highlighter-rouge">prob</code> to get the class index:</p> |
| |
| <div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">max.idx</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">max.col</span><span class="p">(</span><span class="n">t</span><span class="p">(</span><span class="n">prob</span><span class="p">))</span><span class="w"> |
| </span><span class="n">max.idx</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code> ## [1] 89 |
| </code></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="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">synsets</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">readLines</span><span class="p">(</span><span class="s2">"Inception/synset.txt"</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>Let’s see what the image really is:</p> |
| |
| <div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> </span><span class="n">print</span><span class="p">(</span><span class="n">paste0</span><span class="p">(</span><span class="s2">"Predicted Top-class: "</span><span class="p">,</span><span class="w"> </span><span class="n">synsets</span><span class="w"> </span><span class="p">[[</span><span class="n">max.idx</span><span class="p">]]))</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code> ## [1] "Predicted Top-class: n01818515 macaw" |
| </code></pre></div></div> |
| |
| <p>It’s a macaw!</p> |
| |
| <h2 id="reference">Reference</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> |
| |
| <h2 id="next-steps">Next Steps</h2> |
| <ul> |
| <li><a href="https://mxnet.io/tutorials/r/mnistCompetition.html">Handwritten Digits Classification Competition</a></li> |
| <li><a href="https://mxnet.io/tutorials/r/charRnnModel.html">Character Language Model using RNN</a></li> |
| </ul> |
| </div> |
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