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
Code for our CVPR 2021 paper "MetaCam+DSCE"

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification (CVPR'21) Introduction Code for our CVPR 2021

FlyingRoastDuck 59 Oct 31, 2022
source code for 'Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge' by A. Shah, K. Shanmugam, K. Ahuja

Source code for "Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge" Reference: Abhin Shah, Karthikeyan Shanmugam, Kartik Ahu

Abhin Shah 1 Jun 03, 2022
Codes for ACL-IJCNLP 2021 Paper "Zero-shot Fact Verification by Claim Generation"

Zero-shot-Fact-Verification-by-Claim-Generation This repository contains code and models for the paper: Zero-shot Fact Verification by Claim Generatio

Liangming Pan 47 Jan 01, 2023
PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020).

Scaffold-Federated-Learning PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020). Environment numpy=

KI 30 Dec 29, 2022
Practical Single-Image Super-Resolution Using Look-Up Table

Practical Single-Image Super-Resolution Using Look-Up Table [Paper] Dependency Python 3.6 PyTorch glob numpy pillow tqdm tensorboardx 1. Training deep

Younghyun Jo 116 Dec 23, 2022
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

GCNet for Object Detection By Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu. This repo is a official implementation of "GCNet: Non-local Networ

Jerry Jiarui XU 1.1k Dec 29, 2022
Learning Neural Network Subspaces

Learning Neural Network Subspaces Welcome to the codebase for Learning Neural Network Subspaces by Mitchell Wortsman, Maxwell Horton, Carlos Guestrin,

Apple 117 Nov 17, 2022
Implement face detection, and age and gender classification, and emotion classification.

YOLO Keras Face Detection Implement Face detection, and Age and Gender Classification, and Emotion Classification. (image from wider face dataset) Ove

Chloe 10 Nov 14, 2022
PHOTONAI is a high level python API for designing and optimizing machine learning pipelines.

PHOTONAI is a high level python API for designing and optimizing machine learning pipelines. We've created a system in which you can easily select and

Medical Machine Learning Lab - University of Münster 57 Nov 12, 2022
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-t

Facebook Research 5.1k Jan 04, 2023
Angle data is a simple data type.

angledat Angle data is a simple data type. Installing + using Put angledat.py in the main dir of your project. Import it and use. Comments Comments st

1 Jan 05, 2022
Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021)

Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021) By Jinhyung Park, Dohae Lee, In-Kwon Lee from Yonsei University (Seoul,

Jinhyung Park 0 Jan 09, 2022
Team Enigma at ArgMining 2021 Shared Task: Leveraging Pretrained Language Models for Key Point Matching

Team Enigma at ArgMining 2021 Shared Task: Leveraging Pretrained Language Models for Key Point Matching This is our attempt of the shared task on Quan

Manav Nitin Kapadnis 12 Jul 08, 2022
Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding

Relational Self-Attention: What's Missing in Attention for Video Understanding This repository is the official implementation of "Relational Self-Atte

mandos 43 Dec 07, 2022
Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

DIFFNet This repo is for Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised de

Hang 94 Dec 25, 2022
Commonsense Ability Tests

CATS Commonsense Ability Tests Dataset and script for paper Evaluating Commonsense in Pre-trained Language Models Use making_sense.py to run the exper

XUHUI ZHOU 28 Oct 19, 2022
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

10 Dec 14, 2022
We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

Deep GNN, Shallow Sampling Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, R

Facebook Research 117 Dec 20, 2022
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

35 Dec 28, 2022
Machine learning and Deep learning models, deploy on telegram (the best social media)

Semi Intelligent BOT The project involves : Classifying fake news Classifying objects such as aeroplane, automobile, bird, cat, deer, dog, frog, horse

MohammadReza Norouzi 5 Mar 06, 2022