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<div class="title">CaffeSupport </div> </div>
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<div class="textblock"><p>#Caffe layers supported by the Arm NN SDK This reference guide provides a list of Caffe layers the Arm NN SDK currently supports.</p>
<p>Although some other neural networks might work, Arm tests the Arm NN SDK with Caffe implementations of the following neural networks:</p>
<ul>
<li>AlexNet.</li>
<li>Cifar10.</li>
<li>Inception-BN.</li>
<li>Resnet_50, Resnet_101 and Resnet_152.</li>
<li>VGG_CNN_S, VGG_16 and VGG_19.</li>
<li>Yolov1_tiny.</li>
<li>Lenet.</li>
<li>MobileNetv1.</li>
</ul>
<p>The Arm NN SDK supports the following machine learning layers for Caffe networks:</p>
<ul>
<li>BatchNorm, in inference mode.</li>
<li><p class="startli">Convolution, excluding the Dilation Size, Weight Filler, Bias Filler, Engine, Force nd_im2col, and Axis parameters.</p>
<p class="startli">Caffe doesn't support depthwise convolution, the equivalent layer is implemented through the notion of groups. ArmNN supports groups this way:</p><ul>
<li>when group=1, it is a normal conv2d</li>
<li>when group=#input_channels, we can replace it by a depthwise convolution</li>
<li>when group>1 && group<#input_channels, we need to split the input into the given number of groups, apply a separate convolution and then merge the results</li>
</ul>
</li>
<li>Concat, along the channel dimension only.</li>
<li>Dropout, in inference mode.</li>
<li>Eltwise, excluding the coeff parameter.</li>
<li>Inner Product, excluding the Weight Filler, Bias Filler, Engine, and Axis parameters.</li>
<li>Input.</li>
<li>LRN, excluding the Engine parameter.</li>
<li>Pooling, excluding the Stochastic Pooling and Engine parameters.</li>
<li>ReLU.</li>
<li>Scale.</li>
<li>Softmax, excluding the Axis and Engine parameters.</li>
<li>Split.</li>
</ul>
<p>More machine learning layers will be supported in future releases. </p>
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