Train the HRNet model on ImageNet

Related tags

Deep Learningimagenetimage-classificationhigh-resolution-nethrnets
Overview

High-resolution networks (HRNets) for Image classification

News

Introduction

This is the official code of high-resolution representations for ImageNet classification. We augment the HRNet with a classification head shown in the figure below. First, the four-resolution feature maps are fed into a bottleneck and the number of output channels are increased to 128, 256, 512, and 1024, respectively. Then, we downsample the high-resolution representations by a 2-strided 3x3 convolution outputting 256 channels and add them to the representations of the second-high-resolution representations. This process is repeated two times to get 1024 channels over the small resolution. Last, we transform 1024 channels to 2048 channels through a 1x1 convolution, followed by a global average pooling operation. The output 2048-dimensional representation is fed into the classifier.

ImageNet pretrained models

HRNetV2 ImageNet pretrained models are now available!

model #Params GFLOPs top-1 error top-5 error Link
HRNet-W18-C-Small-v1 13.2M 1.49 27.7% 9.3% OneDrive/BaiduYun(Access Code:v3sw)
HRNet-W18-C-Small-v2 15.6M 2.42 24.9% 7.6% OneDrive/BaiduYun(Access Code:bnc9)
HRNet-W18-C 21.3M 3.99 23.2% 6.6% OneDrive/BaiduYun(Access Code:r5xn)
HRNet-W30-C 37.7M 7.55 21.8% 5.8% OneDrive/BaiduYun(Access Code:ajc1)
HRNet-W32-C 41.2M 8.31 21.5% 5.8% OneDrive/BaiduYun(Access Code:itc1)
HRNet-W40-C 57.6M 11.8 21.1% 5.5% OneDrive/BaiduYun(Access Code:i58x)
HRNet-W44-C 67.1M 13.9 21.1% 5.6% OneDrive/BaiduYun(Access Code:3imd)
HRNet-W48-C 77.5M 16.1 20.7% 5.5% OneDrive/BaiduYun(Access Code:68g2)
HRNet-W64-C 128.1M 26.9 20.5% 5.4% OneDrive/BaiduYun(Access Code:6kw4)

Newly added checkpoints:

model #Params GFLOPs top-1 error Link
HRNet-W18-C (w/ CosineLR + CutMix + 300epochs) 21.3M 3.99 22.1% Link
HRNet-W48-C (w/ CosineLR + CutMix + 300epochs) 77.5M 16.1 18.9% Link
HRNet-W18-C-ssld (converted from PaddlePaddle) 21.3M 3.99 18.8% Link
HRNet-W48-C-ssld (converted from PaddlePaddle) 77.5M 16.1 16.4% Link

In the above Table, the first 2 checkpoints are trained with CosineLR, CutMix data augmentation and for longer epochs, i.e., 300epochs. The other two checkpoints are converted from PaddleClas. Please refer to SSLD tutorial for more details.

Quick start

Install

  1. Install PyTorch=0.4.1 following the official instructions
  2. git clone https://github.com/HRNet/HRNet-Image-Classification
  3. Install dependencies: pip install -r requirements.txt

Data preparation

You can follow the Pytorch implementation: https://github.com/pytorch/examples/tree/master/imagenet

The data should be under ./data/imagenet/images/.

Train and test

Please specify the configuration file.

For example, train the HRNet-W18 on ImageNet with a batch size of 128 on 4 GPUs:

python tools/train.py --cfg experiments/cls_hrnet_w18_sgd_lr5e-2_wd1e-4_bs32_x100.yaml

For example, test the HRNet-W18 on ImageNet on 4 GPUs:

python tools/valid.py --cfg experiments/cls_hrnet_w18_sgd_lr5e-2_wd1e-4_bs32_x100.yaml --testModel hrnetv2_w18_imagenet_pretrained.pth

Other applications of HRNet

Citation

If you find this work or code is helpful in your research, please cite:

@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}

@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and 
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and 
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal   = {TPAMI}
  year={2019}
}

Reference

[1] Deep High-Resolution Representation Learning for Visual Recognition. Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao. Accepted by TPAMI. download

Comments
Releases(PretrainedWeights)
Owner
HRNet
Code for pose estimation is available at https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
HRNet
GitHub Repository https://jingdongwang2017.github.io/Projects/HRNet/
MoveNet Single Pose on DepthAI

MoveNet Single Pose tracking on DepthAI Running Google MoveNet Single Pose models on DepthAI hardware (OAK-1, OAK-D,...). A convolutional neural netwo

64 Dec 29, 2022
Python port of R's Comprehensive Dynamic Time Warp algorithm package

Welcome to the dtw-python package Comprehensive implementation of Dynamic Time Warping algorithms. DTW is a family of algorithms which compute the loc

Dynamic Time Warping algorithms 154 Dec 26, 2022
Python PID Tuner - Based on a FOPDT model obtained using a Open Loop Process Reaction Curve

PythonPID_Tuner Step 1: Takes a Process Reaction Curve in csv format - assumes data at 100ms interval (column names CV and PV) Step 2: Makes a rough e

6 Jan 14, 2022
A torch implementation of "Pixel-Level Domain Transfer"

Pixel Level Domain Transfer A torch implementation of "Pixel-Level Domain Transfer". based on dcgan.torch. Dataset The dataset used is "LookBook", fro

Fei Xia 260 Sep 02, 2022
Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021.

Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021. Figure 1: In the process of motion capture (mocap), some joints or even the whole human

Shinny cui 3 Oct 31, 2022
Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Noah Getz 3 Jun 22, 2022
Experiments for distributed optimization algorithms

Network-Distributed Algorithm Experiments -- This repository contains a set of optimization algorithms and objective functions, and all code needed to

Boyue Li 40 Dec 04, 2022
the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

EmbedSeg Introduction This repository hosts the version of the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

JugLab 88 Dec 25, 2022
Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Introduction This repository contains my unofficial reimplementation of the standard ECAPA-TDNN, which is the speaker recognition in VoxCeleb2 dataset

Tao Ruijie 277 Dec 31, 2022
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion

A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion This repo intends to release code for our work: Zhaoyang Lyu*, Zhifeng

Zhaoyang Lyu 68 Jan 03, 2023
Deep Learning ❤️ OneFlow

Deep Learning with OneFlow made easy 🚀 ! Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. User Side Computer V

21 Oct 27, 2022
[ICCV 2021] Relaxed Transformer Decoders for Direct Action Proposal Generation

RTD-Net (ICCV 2021) This repo holds the codes of paper: "Relaxed Transformer Decoders for Direct Action Proposal Generation", accepted in ICCV 2021. N

Multimedia Computing Group, Nanjing University 80 Nov 30, 2022
Collection of machine learning related notebooks to share.

ML_Notebooks Collection of machine learning related notebooks to share. Notebooks GAN_distributed_training.ipynb In this Notebook, TensorFlow's tutori

Sascha Kirch 14 Dec 22, 2022
Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow

xRBM Library Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow Installation Using pip: pip install xrbm Examples Tut

Omid Alemi 55 Dec 29, 2022
TCNN Temporal convolutional neural network for real-time speech enhancement in the time domain

TCNN Pandey A, Wang D L. TCNN: Temporal convolutional neural network for real-time speech enhancement in the time domain[C]//ICASSP 2019-2019 IEEE Int

凌逆战 16 Dec 30, 2022
Earthquake detection via fiber optic cables using deep learning

Earthquake detection via fiber optic cables using deep learning Author: Fantine Huot Getting started Update the submodules After cloning the repositor

Fantine 4 Nov 30, 2022
Compare neural networks by their feature similarity

PyTorch Model Compare A tiny package to compare two neural networks in PyTorch. There are many ways to compare two neural networks, but one robust and

Anand Krishnamoorthy 181 Jan 04, 2023
Progressive Image Deraining Networks: A Better and Simpler Baseline

Progressive Image Deraining Networks: A Better and Simpler Baseline [arxiv] [pdf] [supp] Introduction This paper provides a better and simpler baselin

190 Dec 01, 2022
Single-Stage 6D Object Pose Estimation, CVPR 2020

Overview This repository contains the code for the paper Single-Stage 6D Object Pose Estimation. Yinlin Hu, Pascal Fua, Wei Wang and Mathieu Salzmann.

CVLAB @ EPFL 89 Dec 26, 2022
Generative Adversarial Networks(GANs)

Generative Adversarial Networks(GANs) Vanilla GAN ClusterGAN Vanilla GAN Model Structure Final Generator Structure A MLP with 2 hidden layers of hidde

Zhenbang Feng 2 Nov 05, 2021