IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID,

Related tags

Deep LearningIDM
Overview

Python >=3.7 PyTorch >=1.1

Intermediate Domain Module (IDM)

This repository is the official implementation for IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID, which is accepted by ICCV 2021 (Oral).

IDM achieves state-of-the-art performances on the unsupervised domain adaptation task for person re-ID.

Requirements

Installation

git clone https://github.com/SikaStar/IDM.git
cd IDM/idm/evaluation_metrics/rank_cylib && make all

Prepare Datasets

cd examples && mkdir data

Download the person re-ID datasets Market-1501, DukeMTMC-ReID, MSMT17, PersonX, and UnrealPerson. Then unzip them under the directory like

IDM/examples/data
├── dukemtmc
│   └── DukeMTMC-reID
├── market1501
│   └── Market-1501-v15.09.15
├── msmt17
│   └── MSMT17_V1
├── personx
│   └── PersonX
└── unreal
    ├── list_unreal_train.txt
    └── unreal_vX.Y

Prepare ImageNet Pre-trained Models for IBN-Net

When training with the backbone of IBN-ResNet, you need to download the ImageNet-pretrained model from this link and save it under the path of logs/pretrained/.

mkdir logs && cd logs
mkdir pretrained

The file tree should be

IDM/logs
└── pretrained
    └── resnet50_ibn_a.pth.tar

ImageNet-pretrained models for ResNet-50 will be automatically downloaded in the python script.

Training

We utilize 4 GTX-2080TI GPUs for training. Note that

  • The source and target domains are trained jointly.
  • For baseline methods, use -a resnet50 for the backbone of ResNet-50, and -a resnet_ibn50a for the backbone of IBN-ResNet.
  • For IDM, use -a resnet50_idm to insert IDM into the backbone of ResNet-50, and -a resnet_ibn50a_idm to insert IDM into the backbone of IBN-ResNet.
  • For strong baseline, use --use-xbm to implement XBM (a variant of Memory Bank).

Baseline Methods

To train the baseline methods in the paper, run commands like:

# Naive Baseline
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/run_naive_baseline.sh ${source} ${target} ${arch}

# Strong Baseline
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/run_strong_baseline.sh ${source} ${target} ${arch}

Some examples:

### market1501 -> dukemtmc ###

# ResNet-50
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/run_strong_baseline.sh market1501 dukemtmc resnet50 

# IBN-ResNet-50
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/run_strong_baseline.sh market1501 dukemtmc resnet_ibn50a

Training with IDM

To train the models with our IDM, run commands like:

# Naive Baseline + IDM
CUDA_VISIBLE_DEVICES=0,1,2,3 \
sh scripts/run_idm.sh ${source} ${target} ${arch} ${stage} ${mu1} ${mu2} ${mu3}

# Strong Baseline + IDM
CUDA_VISIBLE_DEVICES=0,1,2,3 \
sh scripts/run_idm_xbm.sh ${source} ${target} ${arch} ${stage} ${mu1} ${mu2} ${mu3}
  • Defaults: --stage 0 --mu1 0.7 --mu2 0.1 --mu3 1.0

Some examples:

### market1501 -> dukemtmc ###

# ResNet-50 + IDM
CUDA_VISIBLE_DEVICES=0,1,2,3 \
sh scripts/run_idm_xbm.sh market1501 dukemtmc resnet50_idm 0 0.7 0.1 1.0 

# IBN-ResNet-50 + IDM
CUDA_VISIBLE_DEVICES=0,1,2,3 \
sh scripts/run_idm_xbm.sh market1501 dukemtmc resnet_ibn50a_idm 0 0.7 0.1 1.0

Evaluation

We utilize 1 GTX-2080TI GPU for testing. Note that

  • use --dsbn for domain adaptive models, and add --test-source if you want to test on the source domain;
  • use -a resnet50 for the backbone of ResNet-50, and -a resnet_ibn50a for the backbone of IBN-ResNet.
  • use -a resnet50_idm for ResNet-50 + IDM, and -a resnet_ibn50a_idm for IBN-ResNet + IDM.

To evaluate the baseline model on the target-domain dataset, run:

CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn -d ${dataset} -a ${arch} --resume ${resume} 

To evaluate the baseline model on the source-domain dataset, run:

CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn --test-source -d ${dataset} -a ${arch} --resume ${resume} 

To evaluate the IDM model on the target-domain dataset, run:

CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn-idm -d ${dataset} -a ${arch} --resume ${resume} --stage ${stage} 

To evaluate the IDM model on the source-domain dataset, run:

CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn-idm --test-source -d ${dataset} -a ${arch} --resume ${resume} --stage ${stage} 

Some examples:

### market1501 -> dukemtmc ###

# evaluate the target domain "dukemtmc" on the strong baseline model
CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn  -d dukemtmc -a resnet50 \
--resume logs/resnet50_strong_baseline/market1501-TO-dukemtmc/model_best.pth.tar 

# evaluate the source domain "market1501" on the strong baseline model
CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn --test-source  -d market1501 -a resnet50 \
--resume logs/resnet50_strong_baseline/market1501-TO-dukemtmc/model_best.pth.tar 

# evaluate the target domain "dukemtmc" on the IDM model (after stage-0)
python3 examples/test.py --dsbn-idm  -d dukemtmc -a resnet50_idm \
--resume logs/resnet50_idm_xbm/market1501-TO-dukemtmc/model_best.pth.tar --stage 0

# evaluate the target domain "dukemtmc" on the IDM model (after stage-0)
python3 examples/test.py --dsbn-idm --test-source  -d market1501 -a resnet50_idm \
--resume logs/resnet50_idm_xbm/market1501-TO-dukemtmc/model_best.pth.tar --stage 0

Acknowledgement

Our code is based on MMT and SpCL. Thanks for Yixiao's wonderful works.

Citation

If you find our work is useful for your research, please kindly cite our paper

@inproceedings{dai2021idm,
  title={IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID},
  author={Dai, Yongxing and Liu, Jun and Sun, Yifan and Tong, Zekun and Zhang, Chi and Duan, Ling-Yu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
}

If you have any questions, please leave an issue or contact me: [email protected]

Owner
Yongxing Dai
I am now a fourth-year PhD student at National Engineering Lab for Video Technology in Peking University, Beijing, China
Yongxing Dai
code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

Facebook Research 94 Oct 26, 2022
Medical Image Segmentation using Squeeze-and-Expansion Transformers

Medical Image Segmentation using Squeeze-and-Expansion Transformers Introduction This repository contains the code of the IJCAI'2021 paper 'Medical Im

askerlee 172 Dec 20, 2022
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

DensePose: Dense Human Pose Estimation In The Wild Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos [densepose.org] [arXiv] [BibTeX] Dense human pos

Meta Research 6.4k Jan 01, 2023
QQ Browser 2021 AI Algorithm Competition Track 1 1st Place Program

QQ Browser 2021 AI Algorithm Competition Track 1 1st Place Program

249 Jan 03, 2023
Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation

Kim Seonghyeon 502 Jan 03, 2023
BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins

BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins Deep learning has brought most profound contributio

Narinder Singh Punn 12 Dec 04, 2022
Embodied Intelligence via Learning and Evolution

Embodied Intelligence via Learning and Evolution This is the code for the paper Embodied Intelligence via Learning and Evolution Agrim Gupta, Silvio S

Agrim Gupta 111 Dec 13, 2022
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model This repository is the official PyTorch implementation of GraphRNN, a graph gene

Jiaxuan 568 Dec 29, 2022
Code for: https://berkeleyautomation.github.io/bags/

DeformableRavens Code for the paper Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks. Here is the

Daniel Seita 121 Dec 30, 2022
Extreme Dynamic Classifier Chains - XGBoost for Multi-label Classification

Extreme Dynamic Classifier Chains Classifier chains is a key technique in multi-label classification, sinceit allows to consider label dependencies ef

6 Oct 08, 2022
Official PyTorch Implementation of GAN-Supervised Dense Visual Alignment

GAN-Supervised Dense Visual Alignment — Official PyTorch Implementation Paper | Project Page | Video This repo contains training, evaluation and visua

944 Jan 07, 2023
StyleGAN2 Webtoon / Anime Style Toonify

StyleGAN2 Webtoon / Anime Style Toonify Korea Webtoon or Japanese Anime Character Stylegan2 base high Quality 1024x1024 / 512x512 Generate and Transfe

121 Dec 21, 2022
Official code release for ICCV 2021 paper SNARF: Differentiable Forward Skinning for Animating Non-rigid Neural Implicit Shapes.

Official code release for ICCV 2021 paper SNARF: Differentiable Forward Skinning for Animating Non-rigid Neural Implicit Shapes.

235 Dec 26, 2022
Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021) This repository is the official PyTorc

Jingyun Liang 139 Dec 29, 2022
City-seeds - A random generator of cultural characteristics intended to spark ideas and help draw threads

City Seeds This is a random generator of cultural characteristics intended to sp

Aydin O'Leary 2 Mar 12, 2022
Semantic Segmentation in Pytorch. Network include: FCN、FCN_ResNet、SegNet、UNet、BiSeNet、BiSeNetV2、PSPNet、DeepLabv3_plus、 HRNet、DDRNet

🚀 If it helps you, click a star! ⭐ Update log 2020.12.10 Project structure adjustment, the previous code has been deleted, the adjustment will be re-

Deeachain 269 Jan 04, 2023
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

SpaceML 92 Nov 30, 2022
A simple Tensorflow based library for deep and/or denoising AutoEncoder.

libsdae - deep-Autoencoder & denoising autoencoder A simple Tensorflow based library for Deep autoencoder and denoising AE. Library follows sklearn st

Rajarshee Mitra 147 Nov 18, 2022
Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

Shakespeare translations using TensorFlow This is an example of using the new Google's TensorFlow library on monolingual translation going from modern

Motoki Wu 245 Dec 28, 2022