The best models do not use either a bottleneck or an inverted bottleneck. While it is common to see modern mobile networks employ inverted bottlenecks, researchers noticed that using inverted bottlenecks degrades performance. One more vitation from Facebook AI RegNet Models Outperform EfficientNet Models, Run 5x Faster on GPUs The paper is empirically showing, with statistical backing to back the claim, that as a design space, g = 1 might be best avoided even though the MNAS search has found particular instances in which there are good performing models to build upon. That such networks can and do perform excellently is not under question. The fact that AnyNetXb populations showed that g >1 is best, does not conflict with this fact. These convolutions could be understood as group convolutions with group width of 1. MNASNets including MobileNets and EfficientNets extensively use Depthwise convolutions to achieve SoTA performances. Interesting example from RegNet or How to methodologically design effective networks: That fact is in sync with the experiments from EffResNetComparison.ipynb (see above). They claim the network 5 times faster than EfficientNet for some configurations.įor the smaller networks the difference is much lower (~2 times). They train and evaluate the sampled architectures to find what works best. The authors take a rather broad space of possible architectures an sample architectures from that space. The implementation and the trained weights can be found at pycls repo: Medium paper: RegNet or How to methodologically design effective networks.Ī class of models that is designed for fast training and inference. Layers instead of upscaled layers of hi resolution.ĭesigning Network Design Spaces paper by Facebook AI Research (FAIR). Just skip connections are binding low-resoltion bottleneck Linear Bottlenecks - for dimensionality reduction The second layer is a1×1convolution, called a pointwise convolution, which is responsible for building new features through computing linear combinations of the in-put channels. The first layer is called a depthwise convolution, it performs lightweight filtering by applying a single convolutional filter per input channel. The basic idea is to replace a full convolutional operator with a factorized version that splits convolution into two separate layers. They implemented several important improvements to reduce the complexity:ĭepthwise Separable Convolutions - instead of using C_out filters of size W x H x C_in we apply C_in filters of size W x H x 1Īnd then C_out filters of size 1 x 1 x C_in. Paper: MobileNetV2: Inverted Residuals and Linear Bottlenecks, Sander rt al, CVPR 2018 They also beat YOLOv2 for object detection on COCO in terms of quality. Our slightly larger MnasNet-A3 modelachieves better accuracy than ResNet-50, but with4.8×fewerparameters and10×fewermultiply-add cost They claim to be 1.8 times faster than Mobilenet V2 with the same performance. Those guise optimized for real mobile hardware.(see picture) Good - Pareto analysis, provide a range of networks for different speed/quality tradeoff. MnasNet paper MnasNet: Platform-Aware Neural Architecture Search for Mobile, Tan et. Great discussion thread on fast.ai: MNasNet # Here is a notebook with some experiments and critique of the efficient net: on google colab, on github The gists is that EfficientNet is optimized with respect to the number of parameters, not actual Addition/Multiplications or FLOPS on the device. I have seen some doubts about using it on mobile devices. The performance with transfer learning seems pretty good. Pretrained weights can be found for Pytorch and Tensortflow: They focus on the scaling parameters of the network rather than finding good building blocks. EfficientNet #ĮfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksĮxplores the scaling of model hyperpameters to achieve computational effectiveness and performance in standard image classification tasks. Here is some explanation of resnet family: An Overview of ResNet and its Variants by Vincent Fung, 2017. Most of the architectures build upon ideas from ResNet paper Deep Residual Learning for Image Recognition, 2015 I made a small research and want to write down some thoughts. I was asked on an interview that and I didn’t have a prepared answer. In 2020 which architecture should I use for my image classification/tracking/segmentation/… task?
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