Skip to content
Pytorch implementation of newly added convolution
Branch: master
Clone or download
xiangtaiLi support more models
Latest commit 36020c3 Jun 24, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
fig support more models Jun 24, 2019
libs support more models Jun 24, 2019
.gitignore add Octaveconv2 Apr 17, 2019
LICENSE add res2net and res2net-se net Apr 17, 2019 support more models Jun 24, 2019 support more models Jun 24, 2019 modify train code with logger Jun 19, 2019


Pytorch implementation of Octave convolution with other similar operation

This is third parity implementation(un-official) of Following Paper which are talked in Recente_Convolution.pdf:

  1. Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution. paper
  2. Adaptively Connected Neural Networks.(CVPR 2019) paper
  3. Res2net:A New Multi-scale Backbone Architecture paper
  4. ScaleNet:Data-Driven Neuron Allocation for Scale Aggregation Networks paper
  5. SRM : A Style-based Recalibration Module for Convolutional Neural Networks paper


  1. add Res2Net bolock with SE-layer (done)
  2. add Adaptive-Convolution: both pixel-aware and dataset-aware (done)
  3. Train code on Imagenet. (done)
  4. Support more models. ()


check model files under the fig/nn floder.

from lib.nn.OCtaveResnet import resnet50
from lib.nn.res2net import se_resnet50
from lib.nn.AdaptiveConvResnet import PixelAwareResnet50, DataSetAwareResnet50

model = resnet50().cuda()
model = se_resnet50().cuda()
model = PixelAwareResnet50().cuda()
model = DataSetAwareResnet50().cuda()


  1. OctaveConv: MXNet implementation here
  2. AdaptiveCov: Offical tensorflow implementation here
  3. ScaleNet: here
  4. SGENet:here


MIT License
You can’t perform that action at this time.