PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

Related tags

Deep Learningdeep-neural-networksimagenetresnetmobilenetmulti-scalepytorch-implementationoctconviccv2019
Overview

octconv.pytorch

PyTorch implementation of Octave Convolution in Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

ResNet-50/101 on ImageNet

Architecture LR decay strategy Parameters GFLOPs Top-1 / Top-5 Accuracy (%)
ResNet-50 step (90 epochs) 25.557M 4.089 76.010 / 92.834
ResNet-50 cosine (120 epochs) 25.557M 4.089 77.150 / 93.468
Oct-ResNet-50 (alpha=0.5) cosine (120 epochs) 25.557M 2.367 77.640 / 93.662
ResNet-101 cosine (120 epochs) 44.549M 7.801 78.898 / 94.304
Oct-ResNet-101 (alpha=0.5) cosine (120 epochs) 44.549M 3.991 78.794 / 94.330
ResNet-152 cosine (120 epochs) 60.193M 11.514 79.234 / 94.556
Oct-ResNet-152 (alpha=0.5) cosine (120 epochs) 60.193M 5.615 79.258 / 94.480

MobileNet V1 on ImageNet

Architecture LR decay strategy Parameters FLOPs Top-1 / Top-5 Accuracy (%)
MobileNetV1 cosine (150 epochs) 4.232M 568.7M 72.238 / 90.536
Oct-MobileNetV1 cosine (150 epochs) 4.232M 318.2M 71.254 / 89.728

Acknowledgement

Official MXNet implmentation by @cypw

Citation

@InProceedings{Chen_2019_ICCV,
author = {Chen, Yunpeng and Fan, Haoqi and Xu, Bing and Yan, Zhicheng and Kalantidis, Yannis and Rohrbach, Marcus and Yan, Shuicheng and Feng, Jiashi},
title = {Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
Owner
Duo Li
Duo Li
GitHub Repository https://arxiv.org/abs/1904.05049
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