MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Paper summary * They suggest a factorization of standard 3x3 convolutions that is more efficient. * They build a model based on that factorization. The model has hyperparameters to choose higher performance or higher accuracy. ### How * Factorization * They factorize the standard 3x3 convolution into one depthwise 3x3 convolution, followed by a pointwise convoluton. * Normal 3x3 convolution: * Computes per filter and location a weighted average over all filters. * For kernel height `kH`, width `kW` and number of input filters/planes `Fin`, it requires `kH*kW*Fin` computations per location. * Depthwise 3x3 convolution: * Computes per filter and location a weighted average over *one* input filter. E.g. the 13th filter would only computed weighted averages over the 13th input filter/plane and ignore all the other input filters/planes. * This requires `kH*kW*1` computations per location, i.e. drastically less than a normal convolution. * Pointwise convolution: * This is just another name for a normal 1x1 convolution. * This is placed after a depthwise convolution in order to compensate the fact that every (depthwise) filter only sees a single input plane. * As the kernel size is `1`, this is rather fast to compute. * Visualization of normal vs factorized convolution: * ![architecture](https://github.com/aleju/papers/blob/master/neural-nets/images/MobileNets/architecture.jpg?raw=true "architecture") * Models * They use two hyperparameters for their models. * `alpha`: Multiplier for the width in the range `(0, 1]`. A value of 0.5 means that every layer has half as many filters. * `roh`: Multiplier for the resolution. In practice this is simply the input image size, having a value of `{224, 192, 160, 128}`. ### Results * ImageNet * Compared to VGG16, they achieve 1 percentage point less accuracy, while using only about 4% of VGG's multiply and additions (mult-adds) and while using only about 3% of the parameters. * Compared to GoogleNet, they achieve about 1 percentage point more accuracy, while using only about 36% of the mult-adds and 61% of the parameters. * Note that they don't compare to ResNet. * Results for architecture choices vs. accuracy on ImageNet: * ![results imagenet](https://github.com/aleju/papers/blob/master/neural-nets/images/MobileNets/results_imagenet.jpg?raw=true "results imagenet") * Relation between mult-adds and accuracy on ImageNet: * ![mult-adds vs accuracy](https://github.com/aleju/papers/blob/master/neural-nets/images/MobileNets/mult-adds_vs_accuracy.jpg?raw=true "mult-adds vs accuracy") * Object Detection * Their mAP is a bit on COCO when combining MobileNet with SSD (as opposed to using VGG or Inception v2). * Their mAP is quite a bit worse on COCO when combining MobileNet with Faster R-CNN. * Reducing the number of filters (`alpha`) influences the results more than reducing the input image resolution (`roh`). * Making the models shallower influences the results more than making them thinner.
arxiv.org
scholar.google.com
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, Andrew G. and Zhu, Menglong and Chen, Bo and Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and Andreetto, Marco and Adam, Hartwig
arXiv e-Print archive - 2017 via Local Bibsonomy
Keywords: dblp


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