Designing Concise ConvNets with Columnar Stages
Keywords: Convolutional Neural Networks, Columnar Stages, Input Replication, Image Classification, Detection
TL;DR: A simple and accurate ConvNet backbone for resource constraints scenarios
Abstract: In the era of vision Transformers, the recent success of VanillaNet shows the huge
potential of simple and concise convolutional neural networks (ConvNets). Where
such models mainly focus on runtime, it is also crucial to simultaneously focus
on other aspects, e.g., FLOPs, parameters, etc, to strengthen their utility further.
To this end, we introduce a refreshing ConvNet macro design called Columnar
Stage Network (CoSNet). CoSNet has a systematically developed simple and
concise structure, smaller depth, low parameter count, low FLOPs, and attention-
less operations, well suited for resource-constrained deployment. The key novelty
of CoSNet is deploying parallel convolutions with fewer kernels fed by input
replication, using columnar stacking of these convolutions, and minimizing the use
of 1×1 convolution layers. Our comprehensive evaluations show that CoSNet rivals
many renowned ConvNets and Transformer designs under resource-constrained
scenarios. Pretrained models shall be open-sourced.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 5799
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