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data-clipboard-text='NetModel["EfficientNet Trained on ImageNet with AutoAugment"]' > <h1> EfficientNet <span class="action">Trained on</span> <span class="data">ImageNet with AutoAugment</span> </h1> </div> </div> <p class="lead">Identify the main object in an image</p> <div class="details"> <p>Released in 2019, this model utilizes the techniques of AutoAugment data augmentation on the EfficientNet architectures to effectively perform image classification.</p> </div> <p class="netsize"> Number of models: 8 </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'>The models achieve the following accuracies on the original ImageNet validation set.</p><div class='print'><img src='https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/57f8b16716cccc48.png' width='106' height='261' style='width: 6.6250em; height: 16.3125em;' /></div></div></li> </ul> <div class="col main"> <h2 id="Examples">Examples</h2> <div id="notebookButtons" class="example"> <p> <a href="https://www.wolframcloud.com/download/5756ca98-5acb-437c-a54a-4ac118eb0147?extension=always&filename=EfficientNet-Trained-on-ImageNet-with-AutoAugment-1-0-0-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 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/> </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/5756ca98-5acb-437c-a54a-4ac118eb0147?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/407/407735c0-39ae-488c-aa61-27384b2c4551/4579bf5ce3de8253.png" alt="NetModel["EfficientNet Trained on ImageNet with AutoAugment"]" width="412" height="19" style="width: 25.7500em; 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/407/407735c0-39ae-488c-aa61-27384b2c4551/6e92acc4ece0350d.png" width="234" height="44" style="width: 14.6250em; height: 2.7500em;"/></div></td></tr></table></div></div><div class="subsection cell-group"><h3>NetModel parameters</h3><p class="example-text">This model consists of a family of individual nets, each identified by a specific parameter combination. Inspect the available parameters:</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/407/407735c0-39ae-488c-aa61-27384b2c4551/641dfbb49909c41c.png" alt="NetModel["EfficientNet Trained on ImageNet with AutoAugment", \ "ParametersInformation"]" 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[2]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/02557bb4a236bf26.png" width="368" height="248" style="width: 23.0000em; height: 15.5000em;"/></div></td></tr></table></div><p class="example-text">Pick a non-default net by specifying the parameters:</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/407/407735c0-39ae-488c-aa61-27384b2c4551/68afcaf0abd42790.png" alt="NetModel[{"EfficientNet Trained on ImageNet with AutoAugment", "Architecture" -> "B5"}]" width="569" height="19" style="width: 35.5625em; 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/407/407735c0-39ae-488c-aa61-27384b2c4551/76e9cbb47a8e8607.png" width="234" height="44" style="width: 14.6250em; height: 2.7500em;"/></div></td></tr></table></div><p class="example-text">Pick a non-default uninitialized net:</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/407/407735c0-39ae-488c-aa61-27384b2c4551/172ecb14adf6dd39.png" alt="NetModel[{"EfficientNet Trained on ImageNet with AutoAugment", "Architecture" -> "B7"}, "UninitializedEvaluationNet"]" width="569" height="43" style="width: 35.5625em; height: 2.6875em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/6b9d24c239564470.png" width="241" height="44" style="width: 15.0625em; 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[5]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/57e2fd082fc4a104.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/753ee0bc-1c92-48fd-a932-5344450b1d35"] " width="546" height="123" style="width: 34.1250em; height: 7.6875em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/567f00ecf5d9f6b5.png" width="72" height="25" style="width: 4.5000em; 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[6]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/463c01ff57b5a5b3.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[6]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/7337abc375ed7bfb.png" width="483" height="17" style="width: 30.1875em; 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[7]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/09bfdab4ebb18577.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[7]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/6811d402537c84f0.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[8]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/0cd62ee025f795ff.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/c36e9527-1b86-478f-834a-6317d7c453f9"] " width="502" height="165" style="width: 31.3750em; height: 10.3125em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/07cf40be08dda4a9.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[9]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/220700682e41ede6.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/1b300312-5948-4048-99ac-3c6cd3d8a09e"] " width="523" height="69" style="width: 32.6875em; height: 4.3125em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[9]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/1e320ae41fd67062.png" width="54" height="25" style="width: 3.3750em; 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[10]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/4abfd86bbd86b2cf.png" alt="EntityValue[ NetExtract[ NetModel["EfficientNet Trained on ImageNet with AutoAugment"], "Output"][["Labels"]], "Name"]" width="575" height="66" style="width: 35.9375em; height: 4.1250em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/240a3e07749e06a1.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 two 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[11]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/08ada4fb2361c900.png" alt="extractor = Take[NetModel[ "EfficientNet Trained on ImageNet with AutoAugment"], {1, -3}]" width="573" height="19" style="width: 35.8125em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[11]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/286a865b37f5685a.png" width="284" height="44" style="width: 17.7500em; 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[12]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/52f9125cb90a469f.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/f0b9acfe-bd56-40da-b455-12e2806bf6cd"] " width="503" height="579" style="width: 31.4375em; height: 36.1875em;"/></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[13]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/128016cb3e27eb9f.png" alt="FeatureSpacePlot[imgs, FeatureExtractor -> extractor, LabelingSize -> 200, ImageSize -> Full, AspectRatio -> 1/2]" width="584" height="43" style="width: 36.5000em; height: 2.6875em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/2b619a46e44d46b6.png" width="639" height="362" style="width: 39.9375em; height: 22.6250em;"/></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[14]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/07ae7483a2dc21c5.png" alt="NetModel["EfficientNet Trained on ImageNet with AutoAugment"]" width="412" height="19" style="width: 25.7500em; height: 1.1875em;"/></div></td></tr></table><table class="example output"><tr><td class="in-out">Out[14]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/6c46fa6ed58d05cc.png" width="234" height="44" style="width: 14.6250em; height: 2.7500em;"/></div></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/407/407735c0-39ae-488c-aa61-27384b2c4551/6801ec360d77656d.png" alt="weights = NetExtract[ NetModel[ "EfficientNet Trained on ImageNet with AutoAugment"], \ {"stem_conv", "Weights"}];" width="550" height="43" style="width: 34.3750em; 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[16]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/7dcfffda500eb38d.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[16]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/2eac027f571d7360.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 32 images of size 3x3:</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/407/407735c0-39ae-488c-aa61-27384b2c4551/3ff279e84dfc8c5c.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[17]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/2a6d55e6e8c38848.png" width="616" height="69" style="width: 38.5000em; 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 motorcycles and bicycles. Create a test set and a training 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/407/407735c0-39ae-488c-aa61-27384b2c4551/3fb5f3022055f1e2.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/77638936-f3cb-4f6e-b58d-7faa086594e1"] " width="589" height="287" style="width: 36.8125em; height: 17.9375em;"/></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[19]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/2e6c4575c539b52d.png" alt="(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/332638ab-acfd-43de-8955-17e26c8eb2cd"] " width="498" height="103" style="width: 31.1250em; height: 6.4375em;"/></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[20]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/71847c3c72fd1fcc.png" alt="tempNet = Take[NetModel[ "EfficientNet Trained on ImageNet with AutoAugment"], {1, -3}]" width="570" height="19" style="width: 35.6250em; height: 1.1875em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/362d2391dcdc6fea.png" width="284" height="44" style="width: 17.7500em; 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[21]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/58ac38364b248a86.png" alt="newNet = NetChain[<|"pretrainedNet" -> tempNet, "linearNew" -> LinearLayer[], "softmax" -> SoftmaxLayer[]|>, "Output" -> NetDecoder[{"Class", {"bicycle", "motorcycle"}}]]" width="628" height="66" style="width: 39.2500em; height: 4.1250em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/0beba951781665ed.png" width="241" height="44" style="width: 15.0625em; 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[22]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/47052beea10bd97d.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[22]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/1fcada0a36d50c5e.png" width="234" height="44" style="width: 14.6250em; height: 2.7500em;"/></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[23]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/1a5a47f71fa344b1.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[23]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/6d28f6416f72b98e.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[24]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/3b0c5b3a5fced028.png" alt="Information[ NetModel["EfficientNet Trained on ImageNet with AutoAugment"], \ "ArraysElementCounts"]" width="495" height="43" style="width: 30.9375em; 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/407/407735c0-39ae-488c-aa61-27384b2c4551/38aab144f12124fc.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[25]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/71db64a52c85aff4.png" alt="Information[ NetModel["EfficientNet Trained on ImageNet with AutoAugment"], \ "ArraysTotalElementCount"]" width="495" height="43" style="width: 30.9375em; height: 2.6875em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/3e7b2415ebd7b6cb.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[26]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/012fe5e98e5aa7b8.png" alt="Information[ NetModel["EfficientNet Trained on ImageNet with AutoAugment"], \ "LayerTypeCounts"]" width="495" height="43" style="width: 30.9375em; height: 2.6875em;"/></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/407/407735c0-39ae-488c-aa61-27384b2c4551/21659dfcdfab9d7c.png" width="579" height="39" style="width: 36.1875em; height: 2.4375em;"/></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[27]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/0bfcd181a66380e9.png" alt="jsonPath = Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], NetModel["EfficientNet Trained on ImageNet with AutoAugment"], "MXNet"]" width="503" height="43" style="width: 31.4375em; 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/407/407735c0-39ae-488c-aa61-27384b2c4551/34b0a0aa115bd9fe.png" width="74" height="17" style="width: 4.6250em; 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[28]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/6fc62d25669d9149.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[28]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/648028176ad9b1e5.png" width="92" height="17" style="width: 5.7500em; 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[29]:=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/596ea201dfa0e8a0.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[29]=</td><td><div class="img-frame"><img src="https://www.wolframcloud.com/obj/resourcesystem/images/407/407735c0-39ae-488c-aa61-27384b2c4551/7d9b6e5d0f7ff412.png" width="55" height="17" style="width: 3.4375em; height: 1.0625em;"/></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/d3388180-51cf-46e7-a066-7b6004d7d63c?extension=always&filename=EfficientNet-Trained-on-ImageNet-with-AutoAugment" 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/d3388180-51cf-46e7-a066-7b6004d7d63c?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/SummaryOfNewFeaturesIn121.html"> Wolfram Language 12.1 </a> (March 2020) or above </p> <h2 id="Resource-History">Resource History</h2> <ul class="source-metadata"> <li> Date Created: <span class="property">6 November 2020</span> </li> </ul> <h2 id="Reference">Reference</h2> <ul class="reference"> <li> <span> E. Cubuk, B. Zoph, D. Mane, V. Vasudevan, Q. Le, "AutoAugment: Learning Augmentation Policies from Data," arXiv:1805.09501 (2018)<br>M. Tan, Q. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," arXiv:1905.11946 (2019) </span> </li> </span></li> <li><span>Available from: <ul class="indent"><li><a href="https://github.com/tensorflow/tensorflow/blob/v2.3.1/tensorflow/python/keras/applications/efficientnet.py" target="_blank">https://github.com/tensorflow/tensorflow/blob/v2.3.1/tensorflow/python/keras/applications/efficientnet.py</a></li><li><a href="https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet" target="_blank">https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet</a></li></ul></span></li> <li> <span>Rights: <a href="https://github.com/tensorflow/tpu/blob/master/LICENSE" target="_blank">Copyright 2017 The TensorFlow Authors. All rights reserved. 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