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
Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision

MLP-Mixer: An all-MLP Architecture for Vision This repo contains PyTorch implementation of MLP-Mixer: An all-MLP Architecture for Vision. Usage : impo

Rishikesh (ऋषिकेश) 175 Dec 23, 2022
Official code base for the poster "On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation" published in NeurIPS 2021 Workshop (SVRHM)

Self-Supervised Learning (SimCLR) with Biological Plausible Image Augmentations Official code base for the poster "On the use of Cortical Magnificatio

Binxu 8 Aug 17, 2022
Neural network pruning for finding a sparse computational model for controlling a biological motor task.

MothPruning Scientific Overview Originally inspired by biological nervous systems, deep neural networks (DNNs) are powerful computational tools for mo

Olivia Thomas 0 Dec 14, 2022
Code for ICCV 2021 paper "HuMoR: 3D Human Motion Model for Robust Pose Estimation"

Code for ICCV 2021 paper "HuMoR: 3D Human Motion Model for Robust Pose Estimation"

Davis Rempe 367 Dec 24, 2022
Adaptation through prediction: multisensory active inference torque control

Adaptation through prediction: multisensory active inference torque control Submitted to IEEE Transactions on Cognitive and Developmental Systems Abst

Cristian Meo 1 Nov 07, 2022
DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time

DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time Introduction This is official implementation for DR-GAN (IEEE TCS

Kang Liao 18 Dec 23, 2022
A DCGAN to generate anime faces using custom mined dataset

Anime-Face-GAN-Keras A DCGAN to generate anime faces using custom dataset in Keras. Dataset The dataset is created by crawling anime database websites

Pavitrakumar P 190 Jan 03, 2023
My usage of Real-ESRGAN to upscale anime, some test and results in the test_img folder

anime upscaler My usage of Real-ESRGAN to upscale anime, I hope to use this on a proper GPU cuz doing this on CPU is completely shit 😂 , I even tried

Shangar Muhunthan 29 Jan 07, 2023
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models

LMPBT Supplementary code for the Paper entitled ``Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models"

1 Sep 29, 2022
Event sourced bank - A wide-and-shallow example using the Python event sourcing library

Event Sourced Bank A "wide but shallow" example of using the Python event sourci

3 Mar 09, 2022
Must-read Papers on Physics-Informed Neural Networks.

PINNpapers Contributed by IDRL lab. Introduction Physics-Informed Neural Network (PINN) has achieved great success in scientific computing since 2017.

IDRL 330 Jan 07, 2023
A micro-game "flappy bird".

1-o-flappy A micro-game "flappy bird". Gameplays The game will be installed at /usr/bin . The name of it is "1-o-flappy". You can type "1-o-flappy" to

1 Nov 06, 2021
Fast and accurate optimisation for registration with little learningconvexadam

convexAdam Learn2Reg 2021 Submission Fast and accurate optimisation for registration with little learning Excellent results on Learn2Reg 2021 challeng

17 Dec 06, 2022
Code for CVPR2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》

Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data. This is the code of CVPR 2019 paper《Unequal Training for Deep Face Recognition

Zhong Yaoyao 68 Jan 07, 2023
Inflated i3d network with inception backbone, weights transfered from tensorflow

I3D models transfered from Tensorflow to PyTorch This repo contains several scripts that allow to transfer the weights from the tensorflow implementat

Yana 479 Dec 08, 2022
Sequential GCN for Active Learning

Sequential GCN for Active Learning Please cite if using the code: Link to paper. Requirements: python 3.6+ torch 1.0+ pip libraries: tqdm, sklearn, sc

45 Dec 26, 2022
FluidNet re-written with ATen tensor lib

fluidnet_cxx: Accelerating Fluid Simulation with Convolutional Neural Networks. A PyTorch/ATen Implementation. This repository is based on the paper,

JoliBrain 50 Jun 07, 2022
Official implementation for the paper: Multi-label Classification with Partial Annotations using Class-aware Selective Loss

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

99 Dec 27, 2022
PyTorch Code for "Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning"

Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning [Project Page] [Paper] Wenlong Huang1, Igor Mordatch2, Pieter Abbeel1,

Wenlong Huang 40 Nov 22, 2022
An Unbiased Learning To Rank Algorithms (ULTRA) toolbox

Unbiased Learning to Rank Algorithms (ULTRA) This is an Unbiased Learning To Rank Algorithms (ULTRA) toolbox, which provides a codebase for experiment

back 3 Nov 18, 2022