CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

Related tags

Deep LearningTransReID
Overview

Python >=3.5 PyTorch >=1.0

[ICCV2021] TransReID: Transformer-based Object Re-Identification [pdf]

The official repository for TransReID: Transformer-based Object Re-Identification achieves state-of-the-art performances on object re-ID, including person re-ID and vehicle re-ID.

News

  • 2021.12 We improve TransReID via self-supervised pre-training. Please refer to TransReID-SSL
  • 2021.3 We release the code of TransReID.

Pipeline

framework

Abaltion Study of Transformer-based Strong Baseline

framework

Requirements

Installation

pip install -r requirements.txt
(we use /torch 1.6.0 /torchvision 0.7.0 /timm 0.3.2 /cuda 10.1 / 16G or 32G V100 for training and evaluation.
Note that we use torch.cuda.amp to accelerate speed of training which requires pytorch >=1.6)

Prepare Datasets

mkdir data

Download the person datasets Market-1501, MSMT17, DukeMTMC-reID,Occluded-Duke, and the vehicle datasets VehicleID, VeRi-776, Then unzip them and rename them under the directory like

data
├── market1501
│   └── images ..
├── MSMT17
│   └── images ..
├── dukemtmcreid
│   └── images ..
├── Occluded_Duke
│   └── images ..
├── VehicleID_V1.0
│   └── images ..
└── VeRi
    └── images ..

Prepare DeiT or ViT Pre-trained Models

You need to download the ImageNet pretrained transformer model : ViT-Base, ViT-Small, DeiT-Small, DeiT-Base

Training

We utilize 1 GPU for training.

python train.py --config_file configs/transformer_base.yml MODEL.DEVICE_ID "('your device id')" MODEL.STRIDE_SIZE ${1} MODEL.SIE_CAMERA ${2} MODEL.SIE_VIEW ${3} MODEL.JPM ${4} MODEL.TRANSFORMER_TYPE ${5} OUTPUT_DIR ${OUTPUT_DIR} DATASETS.NAMES "('your dataset name')"

Arguments

  • ${1}: stride size for pure transformer, e.g. [16, 16], [14, 14], [12, 12]
  • ${2}: whether using SIE with camera, True or False.
  • ${3}: whether using SIE with view, True or False.
  • ${4}: whether using JPM, True or False.
  • ${5}: choose transformer type from 'vit_base_patch16_224_TransReID',(The structure of the deit is the same as that of the vit, and only need to change the imagenet pretrained model) 'vit_small_patch16_224_TransReID','deit_small_patch16_224_TransReID',
  • ${OUTPUT_DIR}: folder for saving logs and checkpoints, e.g. ../logs/market1501

or you can directly train with following yml and commands:

# DukeMTMC transformer-based baseline
python train.py --config_file configs/DukeMTMC/vit_base.yml MODEL.DEVICE_ID "('0')"
# DukeMTMC baseline + JPM
python train.py --config_file configs/DukeMTMC/vit_jpm.yml MODEL.DEVICE_ID "('0')"
# DukeMTMC baseline + SIE
python train.py --config_file configs/DukeMTMC/vit_sie.yml MODEL.DEVICE_ID "('0')"
# DukeMTMC TransReID (baseline + SIE + JPM)
python train.py --config_file configs/DukeMTMC/vit_transreid.yml MODEL.DEVICE_ID "('0')"
# DukeMTMC TransReID with stride size [12, 12]
python train.py --config_file configs/DukeMTMC/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')"

# MSMT17
python train.py --config_file configs/MSMT17/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')"
# OCC_Duke
python train.py --config_file configs/OCC_Duke/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')"
# Market
python train.py --config_file configs/Market/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')"
# VeRi
python train.py --config_file configs/VeRi/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')"

# VehicleID (The dataset is large and we utilize 4 v100 GPUs for training )
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port 66666 train.py --config_file configs/VehicleID/vit_transreid_stride.yml MODEL.DIST_TRAIN True
#  or using following commands:
Bash dist_train.sh

Tips: For person datasets with size 256x128, TransReID with stride occupies 12GB GPU memory and TransReID occupies 7GB GPU memory.

Evaluation

python test.py --config_file 'choose which config to test' MODEL.DEVICE_ID "('your device id')" TEST.WEIGHT "('your path of trained checkpoints')"

Some examples:

# DukeMTMC
python test.py --config_file configs/DukeMTMC/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')"  TEST.WEIGHT '../logs/duke_vit_transreid_stride/transformer_120.pth'
# MSMT17
python test.py --config_file configs/MSMT17/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT '../logs/msmt17_vit_transreid_stride/transformer_120.pth'
# OCC_Duke
python test.py --config_file configs/OCC_Duke/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT '../logs/occ_duke_vit_transreid_stride/transformer_120.pth'
# Market
python test.py --config_file configs/Market/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')"  TEST.WEIGHT '../logs/market_vit_transreid_stride/transformer_120.pth'
# VeRi
python test.py --config_file configs/VeRi/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT '../logs/veri_vit_transreid_stride/transformer_120.pth'

# VehicleID (We test 10 times and get the final average score to avoid randomness)
python test.py --config_file configs/VehicleID/vit_transreid_stride.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT '../logs/vehicleID_vit_transreid_stride/transformer_120.pth'

Trained Models and logs (Size 256)

framework

Datasets MSMT17 Market Duke OCC_Duke VeRi VehicleID
Model mAP | R1 mAP | R1 mAP | R1 mAP | R1 mAP | R1 R1 | R5
Baseline(ViT) 61.8 | 81.8 87.1 | 94.6 79.6 | 89.0 53.8 | 61.1 79.0 | 96.6 83.5 | 96.7
model | log model | log model | log model | log model | log model | test
TransReID*(ViT) 67.8 | 85.3 89.0 | 95.1 82.2 | 90.7 59.5 | 67.4 82.1 | 97.4 85.2 | 97.4
model | log model | log model | log model | log model | log model | test
TransReID*(DeiT) 66.3 | 84.0 88.5 | 95.1 81.9 | 90.7 57.7 | 65.2 82.4 | 97.1 86.0 | 97.6
model | log model | log model | log model | log model | log model | test

Note: We reorganize code and the performances are slightly different from the paper's.

Acknowledgement

Codebase from reid-strong-baseline , pytorch-image-models

We import veri776 viewpoint label from repo: https://github.com/Zhongdao/VehicleReIDKeyPointData

Citation

If you find this code useful for your research, please cite our paper

@InProceedings{He_2021_ICCV,
    author    = {He, Shuting and Luo, Hao and Wang, Pichao and Wang, Fan and Li, Hao and Jiang, Wei},
    title     = {TransReID: Transformer-Based Object Re-Identification},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {15013-15022}
}

Contact

If you have any question, please feel free to contact us. E-mail: [email protected] , [email protected]

Owner
DamoCV
CV team of DAMO academy
DamoCV
GitHub Repository
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

CSA: Contextual Similarity Aggregation with Self-attention for Visual Re-ranking PyTorch training code for CSA (Contextual Similarity Aggregation). We

Hui Wu 19 Oct 21, 2022
Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]

RTFM This repo contains the Pytorch implementation of our paper: Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Lear

Yu Tian 242 Jan 08, 2023
Code for EMNLP2021 paper "Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training"

VoCapXLM Code for EMNLP2021 paper Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training Environment DockerFile: dancingso

Bo Zheng 15 Jul 28, 2022
Open source code for Paper "A Co-Interactive Transformer for Joint Slot Filling and Intent Detection"

A Co-Interactive Transformer for Joint Slot Filling and Intent Detection This repository contains the PyTorch implementation of the paper: A Co-Intera

67 Dec 05, 2022
Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Jacob 27 Oct 23, 2022
Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN)

Flickr-Faces-HQ Dataset (FFHQ) Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative

NVIDIA Research Projects 2.9k Dec 28, 2022
Python 3 module to print out long strings of text with intervals of time inbetween

Python-Fastprint Python 3 module to print out long strings of text with intervals of time inbetween Install: pip install fastprint Sync Usage: from fa

Kainoa Kanter 2 Jun 27, 2022
Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)

Pytorch implementation of Relational Networks - A simple neural network module for relational reasoning Implemented & tested on Sort-of-CLEVR task. So

Kim Heecheol 800 Dec 05, 2022
This repository provides the code for MedViLL(Medical Vision Language Learner).

MedViLL This repository provides the code for MedViLL(Medical Vision Language Learner). Our proposed architecture MedViLL is a single BERT-based model

SuperSuperMoon 39 Jan 05, 2023
Hierarchical Time Series Forecasting with a familiar API

scikit-hts Hierarchical Time Series with a familiar API. This is the result from not having found any good implementations of HTS on-line, and my work

Carlo Mazzaferro 204 Dec 17, 2022
Multi-Scale Progressive Fusion Network for Single Image Deraining

Multi-Scale Progressive Fusion Network for Single Image Deraining (MSPFN) This is an implementation of the MSPFN model proposed in the paper (Multi-Sc

Kuijiang 128 Nov 21, 2022
Official implementation of Unfolded Deep Kernel Estimation for Blind Image Super-resolution.

Unfolded Deep Kernel Estimation for Blind Image Super-resolution Hongyi Zheng, Hongwei Yong, Lei Zhang, "Unfolded Deep Kernel Estimation for Blind Ima

Z80 15 Dec 26, 2022
Rethinking Nearest Neighbors for Visual Classification

Rethinking Nearest Neighbors for Visual Classification arXiv Environment settings Check out scripts/env_setup.sh Setup data Download the following fin

Menglin Jia 29 Oct 11, 2022
Code of paper "CDFI: Compression-Driven Network Design for Frame Interpolation", CVPR 2021

CDFI (Compression-Driven-Frame-Interpolation) [Paper] (Coming soon...) | [arXiv] Tianyu Ding*, Luming Liang*, Zhihui Zhu, Ilya Zharkov IEEE Conference

Tianyu Ding 95 Dec 04, 2022
Official Repository for the paper "Improving Baselines in the Wild".

iWildCam and FMoW baselines (WILDS) This repository was originally forked from the official repository of WILDS datasets (commit 7e103ed) For general

Kazuki Irie 3 Nov 24, 2022
Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Improving Transferability of Representations via Augmentation-Aware Self-Supervision Accepted to NeurIPS 2021 TL;DR: Learning augmentation-aware infor

hankook 38 Sep 16, 2022
YKKDetector For Python

YKKDetector OpenCVを利用した機械学習データをもとに、VRChatのスクリーンショットなどからＹＫＫさん（もとい「幽狐族のお姉様」）を検出できるソフトウェアです。 マニュアル こちらから実行環境のセットアップから解説する詳細なマニュアルをご覧いただけます。 ライセンス 本ソフトウェア

あんふぃとらいと 5 Dec 07, 2021
Real-CUGAN - Real Cascade U-Nets for Anime Image Super Resolution

Real Cascade U-Nets for Anime Image Super Resolution 中文 | English 🔥 Real-CUGAN

tarsin 111 Dec 28, 2022
BERTMap: A BERT-Based Ontology Alignment System

BERTMap: A BERT-based Ontology Alignment System Important Notices The relevant paper was accepted in AAAI-2022. Arxiv version is available at: https:/

KRR 36 Dec 24, 2022
Code for the paper Learning the Predictability of the Future

Learning the Predictability of the Future Code from the paper Learning the Predictability of the Future. Website of the project in hyperfuture.cs.colu

Computer Vision Lab at Columbia University 139 Nov 18, 2022