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VGG-19 - Wolfram Neural Net Repository
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data-clipboard-text='NetModel["VGG-19 Trained on ImageNet Competition Data"]' > <h1> VGG-19 <span class="action">Trained on</span> <span class="data">ImageNet Competition Data</span> </h1> </div> </div> <p class="lead">Identify the main object in an image</p> <div class="details"> <p>Released in 2014 by the Visual Geometry Group at the University of Oxford, this family of architectures achieved second place for the 2014 ImageNet Classification competition. It is noteworthy for its extremely simple structure, being a simple linear chain of layers, with all the convolutional layers having a kernel size of 3x3. Despite this simple structure, it achieves competitive classification accuracy compared to more complicated nets (such as GoogLeNet), although at the cost of slower evaluation speed and much larger net size.</p> </div> <p class="netsize"> Number of layers: 46 | Parameter count: 143,667,240 | Trained size: 575 MB | </p> <h2 id="training-set-info">Training Set Information</h2> <ul> <li> <a href="http://www.image-net.org/challenges/LSVRC/2012/">ImageNet Large Scale Visual Recognition Challenge 2012</a> classification dataset, consisting of 1.2 million training images, with 1,000 classes of objects. </li> </ul> <h2 id="training-set-info">Performance</h2> <ul> <li><p>This model achieves 75.2% top-1 and 92.5% top-5 accuracy on the <a href="http://www.image-net.org/challenges/LSVRC/2012/" target="_blank">ImageNet Large Scale Visual Recognition Challenge 2012</a> dataset.</p></li> </ul> <div class="col main"> <h2 id="Examples">Examples</h2> <div id="notebookButtons" class="example"> <p> <a href="https://www.wolframcloud.com/download/6aee5da8-7127-4b2b-acab-3c520431cb53?extension=always&filename=VGG-19-Trained-on-ImageNet-Competition-Data-1-1-0-examples" target="notebookButton" data-toggle="tooltip" data-placement="bottom" title="Download Example Notebook" > <svg xmlns="http://www.w3.org/2000/svg" class="notebook-download" width="26" height="32" viewBox="0 0 26 32" role="presentation" > <path class="fill" fill="#598527" d="M3.5 0C2.3 0 1.27.86 1.04 2H0v3h1v1H0v3h1v1H0v3h1v1H0v3h1v1H0v3h1v1H0v3h1v1H0v3h1v.5C1 30.9 2.12 32 3.5 32h20c1.4 0 2.5-1.1 2.5-2.5v-27C26 1.1 24.88 0 23.5 0h-20zm0 .98h20c.85 0 1.52.67 1.52 1.5v26.03c0 .85-.67 1.52-1.5 1.52H3.48c-.85 0-1.52-.67-1.52-1.5V2.48c0-.85.67-1.52 1.5-1.52zM19.54 15.15L22 12.4l-3.6-.77.36-3.64-3.4 1.46L13.5 6.3l-1.87 3.16L8.25 8l.37 3.63L5 12.4l2.46 2.75L5 17.88l3.6.78-.36 3.65 3.4-1.47L13.5 24l1.87-3.16 3.38 1.47-.37-3.64L22 17.9zm.65 1.77l-1.66-.56-1.12-1.45 1.44.55zm-5.3 3.38l-1.02 1.73v-1.8l1.06-1.53zm-2.42-8.95l-1.75-.6-1-1.36 1.83.8zm2.96-1.16l1.84-.8-1 1.34-1.76.6zm2.23 1.58l-.9 1.25.06-1.88 1.04-1.4zM13.5 19.5l-1.36-1.95 1.36-1.83 1.36 1.83zm-2.56-5.6l-.07-2.37 2.27.8v2.32zm2.94-1.57l2.26-.8-.06 2.38-2.2.75v-2.32zm-4.55-.55l-.2-2.02 1.04 1.4.05 1.87zm3.6 3.53l-1.36 1.84-2.3-.68 1.46-1.9zm-.85 3.4l1.06 1.53v1.8l-1.02-1.73zm2-3.4l2.2-.73 1.46 1.9-2.3.67zm6.6-2.46l-1.7 1.9-2.03-.74 1.2-1.7zM13.5 7.67l1.3 2.2-1.3 1.65-1.3-1.65zm-7.18 5.17l2.52-.55 1.2 1.7-2 .74zm1.82 2.6l1.44-.53-1.12 1.46-1.65.56zM6.96 17.6l1.7-.58 1.85.53-1.53.48zm2.36 1.05l2.05-.64.05 2.17-2.36 1.02zm6.26 1.52l.05-2.16 2.05.65.26 2.54zm2.44-2.14l-1.53-.48 1.82-.53 1.72.58z" /> </svg> <span class="text" ><span class="line">Download Example </span ><span class="line">Notebook</span></span > </a> </p> <p> <a href="https://www.wolframcloud.com/env/6aee5da8-7127-4b2b-acab-3c520431cb53?src=CloudBasicCopiedContent#sidebar=basic-notebook-links" target="notebookButton" data-toggle="tooltip" data-placement="bottom" title="Open in Wolfram Cloud" > <svg xmlns="http://www.w3.org/2000/svg" class="cloud-open example" width="41" height="32" viewBox="0 0 64 50" role="presentation" > <path class="stroke" fill="none" stroke="#598527" stroke-width="2" stroke-linecap="round" d="M22 42h30c6.08 0 11-4.92 11-11 0-3.85-2.03-7.42-5.33-9.4.22-.7.33-1.4.33-2.1 0-3.87-3.13-7-7-7-.43 0-.86.04-1.3.12C48.35 5.86 42.4 1 35.5 1c-4.74 0-9.18 2.32-11.9 6.2-1.3-.45-2.7-.7-4.1-.7C12.6 6.5 7 12.1 7 19c0 .87.1 1.73.28 2.57C3.45 23.4 1 27.27 1 31.5c0 3.92 2.1 7.54 5.5 9.5" /> <path class="fill" fill="#598527" d="M22.5 19.1L45 20 28.3 35l1-5.5c-5.24 2.27-13.42 9.74-16.1 19.8l-.6-.1c0-12.6 7.6-20.76 14.5-26.3z" /> </svg> <span class="text" ><span class="line">Open in </span ><span class="line">Wolfram Cloud</span></span > </a> </p> </div> <!-- RS_SHINGLE_EXAMPLE_SECTION_START --><div class="example-notebook"><div class="subsection cell-group"><h3>Resource retrieval</h3><p class="example-text">Get the pre-trained net:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[1]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/1d229b9414f1ecf6.png" alt="NetModel["VGG-19 Trained on ImageNet Competition Data"]" width="380" height="19" style="width: 23.7500em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[1]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/797d0f7dc04a7809.png" width="381" height="703" style="width: 23.8125em; height: 43.9375em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>Basic usage</h3><p class="example-text">Classify an image:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[2]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/746e06e39139ccc5.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/364521e5-2245-4bc4-a8b0-f19cb8b761b7"] " width="557" height="84" style="width: 34.8125em; height: 5.2500em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[2]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/385efbc280f171c0.png" width="69" height="25" style="width: 4.3125em; height: 1.5625em;"/></div></td></tr></table></div><p class="example-text">The prediction is an <span class="inline-formula"><a class="reflink" href="https://reference.wolfram.com/language/ref/Entity">Entity</a></span> object, which can be queried:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[3]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/5ef56d2b7ee46902.png" alt="pred["Definition"]" width="114" height="19" style="width: 7.1250em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[3]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/2e511971f857fcec.png" width="536" height="17" style="width: 33.5000em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">Get a list of available properties of the predicted <span class="inline-formula"><a class="reflink" href="https://reference.wolfram.com/language/ref/Entity">Entity</a></span>:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[4]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/292bc988a48ac0b1.png" alt="pred["Properties"]" width="118" height="19" style="width: 7.3750em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[4]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/322ec8d9295ba05f.png" width="521" height="90" style="width: 32.5625em; height: 5.6250em;"/></div></td></tr></table></div><p class="example-text">Obtain the probabilities of the ten most likely entities predicted by the net:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[5]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/1331080833eb6830.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/1a482f67-92cf-4679-8c6a-c111df95e5c8"] " width="513" height="121" style="width: 32.0625em; height: 7.5625em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[5]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/022b8d4bbc5dc25c.png" width="639" height="169" style="width: 39.9375em; height: 10.5625em;"/></div></td></tr></table></div><p class="example-text">An object outside the list of the ImageNet classes will be misidentified:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[6]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/00456942e4d792d7.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/801c3fac-92ee-4a93-8abc-ed92cb92e888"] " width="574" height="123" style="width: 35.8750em; height: 7.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[6]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/4ee6a5c53f8cf286.png" width="105" height="25" style="width: 6.5625em; height: 1.5625em;"/></div></td></tr></table></div><p class="example-text">Obtain the list of names of all available classes:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[7]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/33b3042778014888.png" alt="EntityValue[ NetExtract[NetModel["VGG-19 Trained on ImageNet Competition Data"], "Output"][["Labels"]], "Name"]" width="608" height="43" style="width: 38.0000em; height: 2.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[7]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/7d0ba3d7ed5769a5.png" width="639" height="112" style="width: 39.9375em; height: 7.0000em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>Feature extraction</h3><p class="example-text">Remove the last three layers of the trained net, so that the net produces a vector representation of an image:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[8]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/3673a3a2c0945b0f.png" alt="extractor = Take[NetModel[ "VGG-19 Trained on ImageNet Competition Data"], {1, -4}]" width="541" height="19" style="width: 33.8125em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[8]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/37eefde79a968870.png" width="306" height="57" style="width: 19.1250em; height: 3.5625em;"/></div></td></tr></table></div><p class="example-text">Get a set of images:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[9]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/5a3faafb667768f7.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/0d05015e-a98d-4b9a-ad42-68b4645a2d71"] " width="559" height="451" style="width: 34.9375em; height: 28.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out"></td></tr></table></div><p class="example-text">Visualize the features of a set of images:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[10]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/74d00700496464ec.png" alt="FeatureSpacePlot[imgs, FeatureExtractor -> extractor, LabelingSize -> 100, ImageSize -> 800]" width="581" height="19" style="width: 36.3125em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[10]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/3368981c68d5b855.png" width="800" height="801" style="width: 50.0000em; height: 50.0625em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>Visualize convolutional weights</h3><p class="example-text">Extract the weights of the first convolutional layer in the trained net:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[11]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/1300508208f144c3.png" alt="weights = NetExtract[ NetModel[ "VGG-19 Trained on ImageNet Competition Data"], {"conv1_1", "Weights"}];" width="519" height="43" style="width: 32.4375em; height: 2.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out"></td></tr></table></div><p class="example-text">Visualize the weights as a list of 64 images of size 3x3:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[12]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/13ee5006741ce2c8.png" alt="ImageAdjust[Image[#, Interleaving -> False]] & /@ Normal[weights]" width="420" height="19" style="width: 26.2500em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[12]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/0ebdfe208c6e8c06.png" width="616" height="144" style="width: 38.5000em; height: 9.0000em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>Transfer learning</h3><p class="example-text">Use the pre-trained model to build a classifier for telling apart images of dogs and cats. Create a test set and a training set:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[13]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/0697344879cb1ffc.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/80ac3f76-9b3c-4c1c-bb1f-f24e3c911197"] " width="592" height="274" style="width: 37.0000em; height: 17.1250em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out"></td></tr></table></div><div class="example-frame"><table class="example input"><tr><td class="in-out">In[14]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/48d32ccc221528f4.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/5638a1be-ae09-40e8-8ddf-a22ff7cc8aaa"] " width="521" height="131" style="width: 32.5625em; height: 8.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out"></td></tr></table></div><p class="example-text">Remove the linear layer from the pre-trained net:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[15]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/1455d753d382e3f8.png" alt="tempNet = Take[NetModel[ "VGG-19 Trained on ImageNet Competition Data"], {1, -4}]" width="538" height="19" style="width: 33.6250em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[15]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/53b202b35c258c6f.png" width="306" height="57" style="width: 19.1250em; height: 3.5625em;"/></div></td></tr></table></div><p class="example-text">Create a new net composed of the pre-trained net followed by a linear layer and a softmax layer:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[16]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/1b89764b8beae2cf.png" alt="newNet = NetChain[<|"pretrainedNet" -> tempNet, "linearNew" -> LinearLayer[], "softmax" -> SoftmaxLayer[]|>, "Output" -> NetDecoder[{"Class", {"cat", "dog"}}]]" width="557" height="66" style="width: 34.8125em; height: 4.1250em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[16]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/562b0111d6401d97.png" width="262" height="57" style="width: 16.3750em; height: 3.5625em;"/></div></td></tr></table></div><p class="example-text">Train on the dataset, freezing all the weights except for those in the "linearNew" layer (use <span class="inline-formula"><a class="reflink" href="https://reference.wolfram.com/language/ref/TargetDevice">TargetDevice</a></span> -> "GPU" for training on a GPU):</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[17]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/27b4429f1d57dec4.png" alt="trainedNet = NetTrain[newNet, trainSet, LearningRateMultipliers -> {"linearNew" -> 1, _ -> 0}]" width="582" height="19" style="width: 36.3750em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[17]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/7eed553966dd4f53.png" width="256" height="57" style="width: 16.0000em; height: 3.5625em;"/></div></td></tr></table></div><p class="example-text">Perfect accuracy is obtained on the test set:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[18]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/7207139433ab2112.png" alt="ClassifierMeasurements[trainedNet, testSet, "Accuracy"]" width="356" height="19" style="width: 22.2500em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[18]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/64cc0555a2bf7e1e.png" width="10" height="17" style="width: 0.6250em; height: 1.0625em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>Net information</h3><p class="example-text">Inspect the number of parameters of all arrays in the net:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[19]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/288f10a9e572dcbd.png" alt="NetInformation[ NetModel["VGG-19 Trained on ImageNet Competition Data"], \ "ArraysElementCounts"]" width="486" height="43" style="width: 30.3750em; height: 2.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[19]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/04ad4d60c5201cd6.png" width="639" height="112" style="width: 39.9375em; height: 7.0000em;"/></div></td></tr></table></div><p class="example-text">Obtain the total number of parameters:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[20]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/2ca85f59e558a045.png" alt="NetInformation[ NetModel["VGG-19 Trained on ImageNet Competition Data"], \ "ArraysTotalElementCount"]" width="486" height="43" style="width: 30.3750em; height: 2.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[20]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/4275b9ec30a34f79.png" width="62" height="17" style="width: 3.8750em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">Obtain the layer type counts:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[21]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/1934cb8918da089a.png" alt="NetInformation[ NetModel["VGG-19 Trained on ImageNet Competition Data"], \ "LayerTypeCounts"]" width="486" height="43" style="width: 30.3750em; height: 2.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[21]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/743732b9912ed0c5.png" width="413" height="39" style="width: 25.8125em; height: 2.4375em;"/></div></td></tr></table></div><p class="example-text">Display the summary graphic:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[22]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/5d8e75f6d0f20648.png" alt="NetInformation[ NetModel["VGG-19 Trained on ImageNet Competition Data"], \ "SummaryGraphic"]" width="486" height="43" style="width: 30.3750em; height: 2.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[22]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/57d35d38f32d18c7.png" width="1693" height="38" style="width: 105.8130em; height: 2.3750em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>Export to MXNet</h3><p class="example-text"><span class="inline-formula"><a class="reflink" href="https://reference.wolfram.com/language/ref/Export">Export</a></span> the net into a format that can be opened in MXNet:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[23]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/46e44b12f5cedbb3.png" alt="jsonPath = Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], NetModel["VGG-19 Trained on ImageNet Competition Data"], "MXNet"]" width="472" height="43" style="width: 29.5000em; height: 2.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[23]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/36826453849b4e68.png" width="369" height="17" style="width: 23.0625em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text"><span class="inline-formula"><a class="reflink" href="https://reference.wolfram.com/language/ref/Export">Export</a></span> also creates a <i>net.params </i>file containing parameters:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[24]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/1c790bbd3a2f2da2.png" alt="paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]" width="443" height="19" style="width: 27.6875em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[24]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/7be415db7468b937.png" width="387" height="17" style="width: 24.1875em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">Get the size of the parameter file:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[25]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/1f647a953f3ca74d.png" alt="FileByteCount[paramPath]" width="168" height="19" style="width: 10.5000em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[25]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/0abd3d1df74dfc1e.png" width="62" height="17" style="width: 3.8750em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">The size is similar to the byte count of the resource object:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[26]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/2106393d8e891b63.png" alt="ResourceObject[ "VGG-19 Trained on ImageNet Competition Data"]["ByteCount"]" width="507" height="19" style="width: 31.6875em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[26]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/6f1be2b453cbb306.png" width="62" height="17" style="width: 3.8750em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">Represent the MXNet net as a graph:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[27]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/0480e73adaf065f4.png" alt="Import[jsonPath, {"MXNet", "NodeGraphPlot"}]" width="300" height="19" style="width: 18.7500em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[104]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/5b162ffe2ebe3d0e.png" width="1173" height="555" style="width: 73.3125em; height: 34.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[27]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/2fe/2fe854a5-131f-4d60-8534-27a8fa0ac4e5/5f6cb251443b6fbb.png" width="1223" height="555" style="width: 76.4375em; height: 34.6875em;"/></div></td></tr></table></div></div></div><!-- RS_SHINGLE_EXAMPLE_SECTION_END --> <div id="notebookButtons" class="construction"> <h2 id="Construction-notebook">Construction Notebook</h2> <p> <a href="https://www.wolframcloud.com/download/eb11cc23-ba72-4167-8d2b-d8936ca91947?extension=always&filename=VGG-19-Trained-on-ImageNet-Competition-Data" target="notebookButton" data-toggle="tooltip" data-placement="bottom" title="Download Construction Notebook" > <svg id="iconGroup" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 28 34" class="notebook-download" width="26" height="32" role="presentation" > <path class="fill" fill="#598527" d="M16.67276,8.65568A1.26126,1.26126,0,1,1,15.4115,7.39442,1.26126,1.26126,0,0,1,16.67276,8.65568ZM15.4115,12.655a1.26126,1.26126,0,1,0,1.26126,1.26126A1.26126,1.26126,0,0,0,15.4115,12.655Zm0,4.99574A1.26126,1.26126,0,1,0,16.67276,18.912,1.26126,1.26126,0,0,0,15.4115,17.65072Zm0,4.99574a1.26126,1.26126,0,1,0,1.26126,1.26126A1.26126,1.26126,0,0,0,15.4115,22.64646Zm7.24962-10.2563a1.26126,1.26126,0,1,0,1.26126,1.26126A1.26126,1.26126,0,0,0,22.66112,12.39016Zm0,5.52537a1.26126,1.26126,0,1,0,1.26126,1.26126A1.26126,1.26126,0,0,0,22.66112,17.91553ZM6.83888,9.89229a1.26126,1.26126,0,1,0,1.26126,1.26126A1.26126,1.26126,0,0,0,6.83888,9.89229Zm0,5.26055a1.26126,1.26126,0,1,0,1.26126,1.26126A1.26126,1.26126,0,0,0,6.83888,15.15285Zm0,5.26055a1.26126,1.26126,0,1,0,1.26126,1.26126A1.26126,1.26126,0,0,0,6.83888,20.4134ZM24.5,1A2.48819,2.48819,0,0,1,27,3.5v27A2.47572,2.47572,0,0,1,24.5,33H4.5A2.48819,2.48819,0,0,1,2,30.5V30H1V27H2V26H1V23H2V22H1V19H2V18H1V15H2V14H1V11H2V10H1V7H2V6H1V3H2.04A2.51946,2.51946,0,0,1,4.5,1ZM4.46,1.96a1.50243,1.50243,0,0,0-1.5,1.52V29.53a1.50243,1.50243,0,0,0,1.52,1.5H24.52a1.50243,1.50243,0,0,0,1.5-1.52V3.48a1.50243,1.50243,0,0,0-1.52-1.5H4.5Z" /> <path class="stroke" fill="none" stroke="#598527" stroke-miterlimit="10" stroke-width="0.25px" d="M7.0766,11.391l8.3349-2.51153M7.0766,16.41411l8.3349-2.51153M7.0766,21.43717l8.3349-2.51153m0,5.02306,7.0119-5.02306M15.4115,8.87952l7.0119,5.02306M7.0766,11.391l8.3349,2.51153m0,5.02306L7.0766,11.391m0,0L15.4115,23.9487M7.0766,16.41411l8.3349-7.53459M7.0766,21.43717,15.4115,8.87952M7.0766,16.41411l8.3349,2.51153m0-5.02306L7.0766,21.43717m0-5.02306,8.3349,7.53459m0-5.02306,7.0119-5.02306m-7.0119,0h7.0119m-7.0119,5.02306h7.0119M15.4115,23.9487l7.0119-10.04612M15.4115,8.87952l7.0119,10.04612m-7.0119-5.02306,7.0119,5.02306M7.0766,21.43717l8.3349,2.51153" /> </svg> <span class="text" ><span class="line">Download Construction </span ><span class="line">Notebook</span></span > </a> </p> <p> <a href="https://www.wolframcloud.com/env/eb11cc23-ba72-4167-8d2b-d8936ca91947?src=CloudBasicCopiedContent#sidebar=basic-notebook-links" target="notebookButton" data-toggle="tooltip" data-placement="bottom" title="Open in Wolfram Cloud" > <svg xmlns="http://www.w3.org/2000/svg" class="cloud-open construction" width="41" height="32" viewBox="0 0 64 50" role="presentation" > <path class="stroke" fill="none" stroke="#598527" stroke-width="2" stroke-linecap="round" d="M22 42h30c6.08 0 11-4.92 11-11 0-3.85-2.03-7.42-5.33-9.4.22-.7.33-1.4.33-2.1 0-3.87-3.13-7-7-7-.43 0-.86.04-1.3.12C48.35 5.86 42.4 1 35.5 1c-4.74 0-9.18 2.32-11.9 6.2-1.3-.45-2.7-.7-4.1-.7C12.6 6.5 7 12.1 7 19c0 .87.1 1.73.28 2.57C3.45 23.4 1 27.27 1 31.5c0 3.92 2.1 7.54 5.5 9.5" /> <path class="fill" fill="#598527" d="M22.5 19.1L45 20 28.3 35l1-5.5c-5.24 2.27-13.42 9.74-16.1 19.8l-.6-.1c0-12.6 7.6-20.76 14.5-26.3z" /> </svg> <span class="text" ><span class="line">Open in </span ><span class="line">Wolfram Cloud</span></span > </a> </p> </div> <h2 id="WLVersion">Requirements</h2> <p> <a href="http://reference.wolfram.com/language/guide/SummaryOfNewFeaturesIn112.html"> Wolfram Language 11.2 </a> (September 2017) or above </p> <h2 id="Resource-History">Resource History</h2> <ul class="source-metadata"> <li> Date Created: <span class="property">18 July 2017</span> </li> <li> Latest Update: <span class="property">21 June 2018</span> </li> </ul> <h2 id="Reference">Reference</h2> <ul class="reference"> <li> <span> K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv:1409.1556 (2014) </span> </li> </span></li> <li><span>Available from: <a href="http://www.robots.ox.ac.uk/~vgg/research/very_deep" target="_blank">http://www.robots.ox.ac.uk/~vgg/research/very_deep</a></span></li> <li> <span>Rights: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution 4.0 International (CC BY 4.0)</a> </span> </li> </ul> <footer id="bottom"> <ul> <li> <a href="https://resources.wolframcloud.com/NeuralNetRepository/contact-us" class="contact"> <svg viewBox="0 0 44 32"> <path d="M19 .5A6.5 6.5 0 0 0 12.5 7v4.5H.46l12.04 8.75V25a6.5 6.5 0 0 0 6.5 6.5h18a6.5 6.5 0 0 0 6.5-6.5V7A6.5 6.5 0 0 0 37 .5zm0 1h18A5.5 5.5 0 0 1 42.5 7v18a5.5 5.5 0 0 1-5.5 5.5H19a5.5 5.5 0 0 1-5.5-5.5v-5.25L3.54 12.5h9.96V7A5.5 5.5 0 0 1 19 1.5z" /> <path d="M18 6h20v4H18zM18 14h20v4H18zM18 22h10v4H18z" /></svg ><span class="text">Give Feedback</span> </a> </li> <li> <a href="#top" class="top"> <svg viewBox="0 0 16 16"> <path d="M4 0C1.784 0 0 1.784 0 4v8c0 2.216 1.784 4 4 4h8c2.216 0 4-1.784 4-4V4c0-2.216-1.784-4-4-4H4zm4 3l4 4-1 1-3-3-3 3-1-1 4-4zm0 5l4 4-1 1-3-3-3 3-1-1 4-4z" /></svg ><span class="text">Top</span> </a> </li> </ul> </footer> </div> </div> </main> <footer id="gws-footer"> <div class="wrap"> <p> © 2025 <a href="https://www.wolfram.com/" target="gws-footer">Wolfram</a>. 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