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
Self-Supervised Speech Pre-training and Representation Learning Toolkit.

What's New Sep 2021: We host a challenge in AAAI workshop: The 2nd Self-supervised Learning for Audio and Speech Processing! See SUPERB official site

s3prl 1.6k Jan 08, 2023
Trading Strategies for Freqtrade

Freqtrade Strategies Strategies for Freqtrade, developed primarily in a partnership between @werkkrew and @JimmyNixx from the Freqtrade Discord. Use t

Bryan Chain 242 Jan 07, 2023
Discord-Protect is a simple discord bot allowing you to have some security on your discord server by ordering a captcha to the user who joins your server.

Discord-Protect Discord-Protect is a simple discord bot allowing you to have some security on your discord server by ordering a captcha to the user wh

Tir Omar 2 Oct 28, 2021
Easily pull telemetry data and create beautiful visualizations for analysis.

This repository is a work in progress. Anything and everything is subject to change. Porpo Table of Contents Porpo Table of Contents General Informati

Ryan Dawes 33 Nov 30, 2022
Models, datasets and tools for Facial keypoints detection

Template for Data Science Project This repo aims to give a robust starting point to any Data Science related project. It contains readymade tools setu

girafe.ai 1 Feb 11, 2022
Geometric Vector Perceptron --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Code to accompany Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL T

Dror Lab 85 Dec 29, 2022
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

Octavio Arriaga 5.3k Dec 30, 2022
Federated Learning - Including common test models for federated learning, like CNN, Resnet18 and lstm, controlled by different parser

Federated_Learning 💻 This projest include common test models for federated lear

TianyuQi 10 Dec 11, 2022
A Java implementation of the experiments for the paper "k-Center Clustering with Outliers in Sliding Windows"

OutliersSlidingWindows A Java implementation of the experiments for the paper "k-Center Clustering with Outliers in Sliding Windows" Dataset generatio

PaoloPellizzoni 0 Jan 05, 2022
This is the code related to "Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation" (ICCV 2021).

Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation This is the code relat

39 Sep 23, 2022
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

SMPLify-XMC This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] License Software Copyright Lic

Lea Müller 83 Dec 14, 2022
Official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks".

GN-Transformer AST This is the official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks". Data Prep

Cheng Jun-Yan 10 Nov 26, 2022
Auto grind btdb2 exp for tower

Bloons TD Battles 2 EXP Grinder Auto grind btdb2 exp for towers Setup I suggest checking out every screenshot to see what they are supposed to be, so

Vincent 6 Jul 29, 2022
Finding all things on-prem Microsoft for password spraying and enumeration.

msprobe About Installing Usage Examples Coming Soon Acknowledgements About Finding all things on-prem Microsoft for password spraying and enumeration.

205 Jan 09, 2023
[ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang

Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective [PDF] Wuyang Chen, Xinyu Gong, Zhangyang Wang In ICLR 2

VITA 156 Nov 28, 2022
Enhancing Knowledge Tracing via Adversarial Training

Enhancing Knowledge Tracing via Adversarial Training This repository contains source code for the paper "Enhancing Knowledge Tracing via Adversarial T

Xiaopeng Guo 14 Oct 24, 2022
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021)

Evaluation, Training, Demo, and Inference of DeFMO DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021) Denys Rozumnyi, Martin R. O

Denys Rozumnyi 139 Dec 26, 2022
MediaPipe is a an open-source framework from Google for building multimodal

MediaPipe is a an open-source framework from Google for building multimodal (eg. video, audio, any time series data), cross platform (i.e Android, iOS, web, edge devices) applied ML pipelines. It is

Bhavishya Pandit 3 Sep 30, 2022
Convolutional 2D Knowledge Graph Embeddings resources

ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes

Tim Dettmers 586 Dec 24, 2022
natural image generation using ConvNets

The Eyescream Project Generating Natural Images using Neural Networks. For our research summary on this work, please read the Arxiv paper: http://arxi

Meta Archive 601 Nov 23, 2022