PyTorch

Res2Net: A New Multi-scale Backbone Architecture

Res2Net

The official pytorch implemention of the paper “Res2Net: A New Multi-scale Backbone Architecture”

Our paper is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like
connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range
of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models,
e.g. , ResNet, ResNeXt, BigLittleNet, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models.

Sample

Res2Net module

Useage

Requirement

PyTorch>=0.4.1

Examples

git clone https://github.com/gasvn/Res2Net.git


from res2net import res2net50

model = res2net50(pretrained=True)


 


 




 


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