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data-clipboard-text='NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"]' > <h1> ShuffleNet-V1 <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 2017, ShuffleNet is designed specially for mobile devices with very limited computing power. Its architecture utilizes two new operations: pointwise group convolutions and channel shuffling, greatly reducing computational cost while maintaining accuracy.</p> </div> <p class="netsize"> Number of layers: 203 | Parameter count: 1,420,152 | Trained size: 7 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><div class='example-notebook'><p class='example-text'>This model achieves a top-1 accuracy of 67.4% on the original ImageNet validation set.</p></div></li> </ul> <div class="col main"> <h2 id="Examples">Examples</h2> <div id="notebookButtons" class="example"> <p> <a 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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> <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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/3bbb0a17f456f005.png" alt="NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"]" width="421" height="19" style="width: 26.3125em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/50ef70600679c1a0.png" width="233" height="44" style="width: 14.5625em; height: 2.7500em;"/></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 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BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{88., 102.}, PlotRange->{{0, 88.}, {0, 102.}}]\)]" width="573" height="116" style="width: 35.8125em; height: 7.2500em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/5a721810e8f5511f.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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/5ef56d2b7ee46902.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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/794a19cc5679efbe.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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/292bc988a48ac0b1.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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/33b73f61ecbacfc1.png" width="639" height="139" style="width: 39.9375em; height: 8.6875em;"/></div></td></tr></table></div><p class="example-text">Obtain the probabilities of the 10 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/1f94a4c1b81ca768.png" alt="NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"][\!\(\* GraphicsBox[ 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sF+Q3GhzQDEsXbHHG4cKD9SFoZTOlrA7zpnB64u4NMVJz7HErzEI/54wQgdj 8GsPw67OiRPlFrj2u1Nyw4H6ixaETCVhf02LduUsHZeieL2exRcbxXxxpZZX ktfTQ4EElh1jZ8QxFHa+KKUXmHlIVjnKrFp6CmM5ozxpJx4geh4UPQ+Ijvvl tvuOonjnv7e9x7Y25b4jmO06wM433uatt3ZgeeZtvP2ObH3+ZlSVPF7TWaJq ncnp9CCj0oVQ6bjumUc5GLaPXf77MPN+A7O0d2SuFShGFOzoOcGOxl0cHnub 2NH95IzsFuYK48FCLh+IB1T22FI5aMFT6dxzF6KxjwjCPjiAgaEgYlO8CdLa 4xhmLfwVzKkiL4rXSrCclRkac5aO7ox1iwcOBS4yu+XkTDXjWqOleqmd3cXu 7Eo8gk+1O9HSw87WubO33JbAgSCKhM+LP4in6q4LFfetCZ/35F3h8ohzzhjn 9mOadWd6MIgLneJZfQFE1e0WfpLssTmB8rjwj0WAnHcTFhAflXk1OybaHjiD 2YETmL1zZOt7k5V7D4ie+0Xbw3J65P/fdgkrvPkWu97cwSHxhpg4B2qbtNS2 RgmfemJs9iezwZfyvkAKG9QUb343jfBOdK7kRIYTpzPexrphL0f1B6RLitYt hzHreBfF6lGUV63RTzjz0VwqLxaEfYaiCB06jlvjTqY61NwcT2S035eJ4UDW xrz4fwFwto65 "], {{0, 102.}, {88., 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{88., 102.}, PlotRange->{{0, 88.}, {0, 102.}}]\), {"TopProbabilities", 10}]" width="529" height="158" style="width: 33.0625em; height: 9.8750em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/7ca527751ecd39ba.png" width="639" height="139" style="width: 39.9375em; height: 8.6875em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/0c5d28ffb533898d.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/b55821bc-1846-44b5-9ffe-fe7151218000"] " width="615" height="123" style="width: 38.4375em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/072acf47b91f2d90.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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/624fa8cc8a542848.png" alt="EntityValue[ NetExtract[ NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"], "Output"][["Labels"]], "Name"]" width="582" height="66" style="width: 36.3750em; height: 4.1250em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/5871c2765a06ddc9.png" width="639" height="155" style="width: 39.9375em; height: 9.6875em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>Feature extraction</h3><p class="example-text">Remove the last four 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/191b15d3d5b132b6.png" alt="extractor = NetDrop[NetModel[ "ShuffleNet-V1 Trained on ImageNet Competition Data"], -3]" width="578" height="19" style="width: 36.1250em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/75eb52bfa651c02f.png" width="293" height="44" style="width: 18.3125em; height: 2.7500em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/23a13f2b5e3f79b9.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/7f6bc0e9-78ed-4888-be82-6f35a0f3cd2c"] " width="525" height="355" style="width: 32.8125em; height: 22.1875em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/30830bff0a6f9dac.png" alt="FeatureSpacePlot[imgs, FeatureExtractor -> extractor, LabelingSize -> 100, ImageSize -> 600]" 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/58349c3cb8284d29.png" width="600" height="582" style="width: 37.5000em; height: 36.3750em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/56c74423152a233c.png" alt="weights = NetExtract[ NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"], {1, "Weights"}];" width="560" height="43" style="width: 35.0000em; height: 2.6875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out"></td></tr></table></div><p class="example-text">Show the dimensions of the weights:</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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/1ace273dfed90a85.png" alt="Dimensions[weights]" width="131" height="19" style="width: 8.1875em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/754049442bcb3d65.png" width="59" height="17" style="width: 3.6875em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">Visualize the weights as a list of 24 images of size 3&Cross;3:</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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/716c5214f801d299.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[13]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/20a0338d5efe81d3.png" width="480" height="69" style="width: 30.0000em; height: 4.3125em;"/></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 sunflowers and roses. Create a test set and a training set:</p><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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/14f72902db8b164d.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/12fdab7d-726e-4e6f-883a-2525491f4f86"] " width="620" height="683" style="width: 38.7500em; height: 42.6875em;"/></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[15]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/00245fe3dbd6d075.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/8483869d-902e-4361-9bbd-27cd296211ac"] " width="544" height="153" style="width: 34.0000em; height: 9.5625em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out"></td></tr></table></div><p class="example-text">Remove the last layers from the pre-trained net:</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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/08f6db71ca09acba.png" alt="tempNet = NetDrop[NetModel[ "ShuffleNet-V1 Trained on ImageNet Competition Data"], -2]" width="575" height="19" style="width: 35.9375em; height: 1.1875em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/57cf3a6b00cfda97.png" width="277" height="44" style="width: 17.3125em; height: 2.7500em;"/></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[17]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/5d7fa8d36fa60070.png" alt="newNet = NetChain[<|"pretrainedNet" -> tempNet, "linearNew" -> LinearLayer[], "softmax" -> SoftmaxLayer[]|>, "Output" -> NetDecoder[{"Class", {"sunflower", "rose"}}]]" width="603" height="66" style="width: 37.6875em; height: 4.1250em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/52dc7933c0477a07.png" width="240" height="44" style="width: 15.0000em; height: 2.7500em;"/></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[18]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/47421ed13eca4ccc.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[18]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/48fe72b04ff707a8.png" width="233" height="44" style="width: 14.5625em; height: 2.7500em;"/></div></td></tr></table></div><p class="example-text">Accuracy obtained on the test set:</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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/168b14f7b47faf4a.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[19]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/4775f17d364e915b.png" width="16" height="17" style="width: 1.0000em; 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[20]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/3da9bc447d905214.png" alt="Information[ NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"], "ArraysElementCounts"]" width="505" height="43" style="width: 31.5625em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/10c9b271ac03af86.png" width="639" height="155" style="width: 39.9375em; height: 9.6875em;"/></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[21]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/27b511841d9ab4cc.png" alt="Information[ NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"], "ArraysTotalElementCount"]" width="505" height="43" style="width: 31.5625em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/11cc4503e7423cca.png" width="49" height="17" style="width: 3.0625em; 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[22]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/0d86c33831725710.png" alt="Information[ NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"], "LayerTypeCounts"]" width="505" height="43" style="width: 31.5625em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/4a7a65c97f29a806.png" width="472" height="61" style="width: 29.5000em; height: 3.8125em;"/></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[23]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/5c4d6d5bb2feaa24.png" alt="Information[ NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"], "SummaryGraphic"]" width="505" height="43" style="width: 31.5625em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/4733d317caa68ead.png" width="898" height="48" style="width: 56.1250em; height: 3.0000em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>Export to ONNX</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 to the ONNX format:</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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/462e9a8f8217d026.png" alt="onnxFile = Export[FileNameJoin[{$TemporaryDirectory, "net.onnx"}], NetModel["ShuffleNet-V1 Trained on ImageNet Competition Data"]]" width="451" height="43" style="width: 28.1875em; height: 2.6875em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/2792246d5e0fd2be.png" width="78" height="17" style="width: 4.8750em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">Get the size of the ONNX 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/32f15a31767c1af8.png" alt="FileByteCount[onnxFile]" width="151" height="19" style="width: 9.4375em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/44dce5a165011732.png" width="49" height="17" style="width: 3.0625em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/24b1c767a4fd419a.png" alt="ResourceObject[ "ShuffleNet-V1 Trained on ImageNet Competition Data"]["ByteCount"]" width="548" height="19" style="width: 34.2500em; 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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/5d2c0f8e669d2dcc.png" width="49" height="17" style="width: 3.0625em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">Check some metadata of the ONNX model:</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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/73c8d714c305ed18.png" alt="{OpsetVersion, IRVersion} = {Import[onnxFile, "OperatorSetVersion"], Import[onnxFile, "IRVersion"]}" width="469" height="43" style="width: 29.3125em; height: 2.6875em;"/></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/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/30f556ae943265c1.png" width="189" height="17" style="width: 11.8125em; height: 1.0625em;"/></div></td></tr></table></div><p class="example-text">Import the model back into the Wolfram Language. However, the <a href="https://reference.wolfram.com/language/ref/NetEncoder.html">NetEncoder</a> and <a href="https://reference.wolfram.com/language/ref/NetDecoder.html">NetDecoder</a> will be absent because they are not supported by ONNX:</p><div class="example-frame"><table class="example input"><tr><td class="in-out">In[28]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/10a49876ceb3830e.png" alt="Import[onnxFile]" width="106" height="19" style="width: 6.6250em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[28]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/79c/79cab740-eca5-4921-b1a7-e79f82fb39d8/1ab7222f38331fbd.png" width="305" height="47" style="width: 19.0625em; height: 2.9375em;"/></div></td></tr></table></div></div></div> <div id="notebookButtons" class="construction"> <h2 id="Construction-notebook">Construction Notebook</h2> <p> <a href="https://www.wolframcloud.com/download/c4e3e095-ab5d-480e-99f9-a6488f846461?extension=always&filename=ShuffleNet-V1-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/c4e3e095-ab5d-480e-99f9-a6488f846461?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="https://reference.wolfram.com/language/guide/SummaryOfNewFeaturesIn123.html"> Wolfram Language 12.3 </a> (May 2021) or above </p> <h2 id="Resource-History">Resource History</h2> <ul class="source-metadata"> <li> Date Created: <span class="property">4 June 2021</span> </li> </ul> <h2 id="Reference">Reference</h2> <ul class="reference"> <li> <span> X. Zhang, X. Zhou, M. Lin, J. Sun, "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices," arXiv:1707.01083v2 (2017) </span> </li> </span></li> <li><span>Available from: <a href="https://github.com/onnx/models/tree/master/vision/classification/shufflenet" target="_blank">https://github.com/onnx/models/tree/master/vision/classification/shufflenet</a></span></li> <li> <span>Rights: <a href="https://opensource.org/licenses/BSD-3-Clause" target="_blank">BSD 3-Clause</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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