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
GitHub Repository
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

ChongjianGE 89 Dec 02, 2022
A series of convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python.

imutils A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, and displ

Adrian Rosebrock 4.3k Jan 08, 2023
PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds

PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds PCAM: Product of Cross-Attention Matrices for Rigid Registration of P

valeo.ai 24 May 31, 2022
CCCL: Contrastive Cascade Graph Learning.

CCGL: Contrastive Cascade Graph Learning This repo provides a reference implementation of Contrastive Cascade Graph Learning (CCGL) framework as descr

Xovee Xu 19 Dec 05, 2022
Deep learning based hand gesture recognition using LSTM and MediaPipie.

Hand Gesture Recognition Deep learning based hand gesture recognition using LSTM and MediaPipie. Demo video using PingPong Robot Files Pretrained mode

Brad 24 Nov 11, 2022
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang 1.2k Dec 29, 2022
Pseudo-Visual Speech Denoising

Pseudo-Visual Speech Denoising This code is for our paper titled: Visual Speech Enhancement Without A Real Visual Stream published at WACV 2021. Autho

Sindhu 94 Oct 22, 2022
Semi-SDP Semi-supervised parser for semantic dependency parsing.

Semi-SDP Semi-supervised parser for semantic dependency parsing. This repo contains the code used for the semi-supervised semantic dependency parser i

12 Sep 17, 2021
The code used for the free [email protected] Webinar series on Reinforcement Learning in Finance

Reinforcement Learning in Finance [email protected] Webinar This repository provides the code f

Yves Hilpisch 62 Dec 22, 2022
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On

Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On [Project website] [Dataset] [Video] Abstract We propose a new g

71 Dec 24, 2022
Collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and related datasets

The repository collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and related datasets

Jun Chen 139 Dec 21, 2022
PyTorch implementation of paper: AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer, ICCV 2021.

AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer [Paper] [PyTorch Implementation] [Paddle Implementation] Overview This reposit

148 Dec 30, 2022
Deep motion transfer

animation-with-keypoint-mask Paper The right most square is the final result. Softmax mask (circles): \ Heatmap mask: \ conda env create -f environmen

9 Nov 01, 2022
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Generative Models Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Note: Gen

Agustinus Kristiadi 7k Jan 02, 2023
Code in conjunction with the publication 'Contrastive Representation Learning for Hand Shape Estimation'

HanCo Dataset & Contrastive Representation Learning for Hand Shape Estimation Code in conjunction with the publication: Contrastive Representation Lea

Computer Vision Group, Albert-Ludwigs-Universität Freiburg 38 Dec 13, 2022
Yolox-bytetrack-sample - Python sample of MOT (Multiple Object Tracking) using YOLOX and ByteTrack

yolox-bytetrack-sample YOLOXとByteTrackを用いたMOT(Multiple Object Tracking)のPythonサン

KazuhitoTakahashi 12 Nov 09, 2022
This repository provides the official implementation of 'Learning to ignore: rethinking attention in CNNs' accepted in BMVC 2021.

inverse_attention This repository provides the official implementation of 'Learning to ignore: rethinking attention in CNNs' accepted in BMVC 2021. Le

Firas Laakom 5 Jul 08, 2022
Simulation of moving particles under microscopic imaging

Simulation of moving particles under microscopic imaging Install scipy numpy scikit-image tiffile Run python simulation.py Read result https://imagej

Zehao Wang 2 Dec 14, 2021
Code, Models and Datasets for OpenViDial Dataset

OpenViDial This repo contains downloading instructions for the OpenViDial dataset in 《OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Vis

119 Dec 08, 2022
JAX-based neural network library

Haiku: Sonnet for JAX Overview | Why Haiku? | Quickstart | Installation | Examples | User manual | Documentation | Citing Haiku What is Haiku? Haiku i

DeepMind 2.3k Jan 04, 2023