TransReID: Transformer-based Object Re-Identification

Related tags

Deep LearningTransReID
Overview

Python >=3.5 PyTorch >=1.0

TransReID: Transformer-based Object Re-Identification [arxiv]

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.

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')"

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/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/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/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/transformer_120.pth'

Trained Models and logs (Size 256)

framework

Model MSMT17 Market Duke OCC_Duke VeRi VehicleID
baseline(ViT) model | log model | log model | log model | log TBD TBD
TransReID^*(ViT) model | log model | log model | log model | log TBD TBD
TransReID^*(DeiT) model | log model | log model | log model | log TBD TBD

（We reorganize the code. Now we are running each experiment one by one to make sure that you can reproduce the results in our paper. We will gradually upload the logs and models after training in the following weeks. Thanks for your attention.）

Acknowledgement

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

Citation

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

@article{he2021transreid,
  title={TransReID: Transformer-based Object Re-Identification},
  author={He, Shuting and Luo, Hao and Wang, Pichao and Wang, Fan and Li, Hao and Jiang, Wei},
  journal={arXiv preprint arXiv:2102.04378},
  year={2021}
}

Contact

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

Owner
Zhejiang University
GitHub Repository
Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection".

A2S-USOD Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection". Code will be released upon

15 Dec 16, 2022
SegNet model implemented using keras framework

keras-segnet Implementation of SegNet-like architecture using keras. Current version doesn't support index transferring proposed in SegNet article, so

185 Aug 30, 2022
Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions'

pytorch-inpainting-with-partial-conv Official implementation is released by the authors. Note that this is an ongoing re-implementation and I cannot f

Naoto Inoue 525 Jan 01, 2023
Breaching - Breaching privacy in federated learning scenarios for vision and text

Breaching - A Framework for Attacks against Privacy in Federated Learning This P

Jonas Geiping 139 Jan 03, 2023
Official Repsoitory for "Mish: A Self Regularized Non-Monotonic Neural Activation Function" [BMVC 2020]

Mish: Self Regularized Non-Monotonic Activation Function BMVC 2020 (Official Paper) Notes: (Click to expand) A considerably faster version based on CU

Xa9aX ツ 1.2k Dec 29, 2022
Step by Step on how to create an vision recognition model using LOBE.ai, export the model and run the model in an Azure Function

Step by Step on how to create an vision recognition model using LOBE.ai, export the model and run the model in an Azure Function

El Bruno 3 Mar 30, 2022
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data arXiv This is the code base for weakly supervised NER. We provide a

Amazon 92 Jan 04, 2023
[SIGGRAPH 2020] Attribute2Font: Creating Fonts You Want From Attributes

Attr2Font Introduction This is the official PyTorch implementation of the Attribute2Font: Creating Fonts You Want From Attributes. Paper: arXiv | Rese

Yue Gao 200 Dec 15, 2022
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (EMNLP Founding 2021)

Introduction K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce. Installation PyTor

Xu Song 21 Nov 16, 2022
Vision Transformer and MLP-Mixer Architectures

Vision Transformer and MLP-Mixer Architectures Update (2.7.2021): Added the "When Vision Transformers Outperform ResNets..." paper, and SAM (Sharpness

Google Research 6.4k Jan 04, 2023
Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer"

StyleAttack Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer" Prepare Pois

THUNLP 19 Nov 20, 2022
一套完整的微博舆情分析流程代码，包括微博爬虫、LDA主题分析和情感分析。

已经将项目的关键文件上传，包含微博爬虫、LDA主题分析和情感分析三个部分。 1.微博爬虫 实现微博评论爬取和微博用户信息爬取，一天大概十万条。 2.LDA主题分析 实现文档主题抽取，包括数据清洗及分词、主题数的确定（主题一致性和困惑度）和最优主题模型的选择（暴力搜索）。 3.情感分析 实现评论文本的

182 Jan 02, 2023
Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

1 Jan 16, 2022
An official implementation of the Anchor DETR.

Anchor DETR: Query Design for Transformer-Based Detector Introduction This repository is an official implementation of the Anchor DETR. We encode the

MEGVII Research 276 Dec 28, 2022
Repository for code and dataset for our EMNLP 2021 paper - “So You Think You’re Funny?”: Rating the Humour Quotient in Standup Comedy.

AI-OpenMic Dataset The dataset is available for download via the follwing link. Repository for code and dataset for our EMNLP 2021 paper - “So You Thi

6 Oct 26, 2022
Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers

Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers This is an implementation of A Physics-Informed Vector Quantized Autoencoder for Dat

DreamSoul 3 Sep 12, 2022
Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization

Benjamin Biggs 29 Dec 28, 2022
Predictive Maintenance LSTM

Predictive-Maintenance-LSTM - Predictive maintenance study for Complex case study, we've obtained failure causes by operational error and more deeply by design mistakes.

Amir M. Sadafi 1 Dec 31, 2021
A collection of metrics for evaluating timbre dissimilarity using the TorchMetrics API

Timbre Dissimilarity Metrics A collection of metrics for evaluating timbre dissimilarity using the TorchMetrics API Installation pip install -e . Usag

Ben Hayes 21 Jan 05, 2022
A curated list of neural rendering resources.

Awesome-of-Neural-Rendering A curated list of neural rendering and related resources. Please feel free to pull requests or open an issue to add papers

Zhiwei ZHANG 43 Dec 09, 2022