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Bias Eliminate Domain Adaptive Pedestrian Re-identification [Technique Report]

This repo contains our code for VisDA2020 challenge at ECCV workshop.

Introduction

This work mainly solve the domain adaptive pedestrian re-identification problem by eliminishing the bias from inter-domain gap and intra-domain camera difference.

This project is mainly based on reid-strong-baseline.

Get Started

  1. Clone the repo git clone https://github.com/vimar-gu/Bias-Eliminate-DA-ReID.git
  2. Install dependencies:
  • pytorch >= 1.0.0
  • python >= 3.5
  • torchvision
  • yacs
  1. Prepare dataset. It can be obtained from Simon4Yan/VisDA2020.
  2. We use ResNet-ibn and HRNet as backbones. ImageNet pretrained models can be downloaded in here and here.

Run

If you want to reproduce our results, please refer to [VisDA.md]

Results

The performance on VisDA2020 validation dataset

Method mAP Rank-1 Rank-5 Rank-10
Basline 30.7 59.7 77.5 83.3
+ Domain Adaptation 44.9 75.3 86.7 91.0
+ Finetuning 48.6 79.8 88.3 91.5
+ Post Processing 70.9 86.5 92.8 94.4

Trained models

The models can be downloaded from:

The camera models can be downloaded from:

Some tips

  • By our experience, there can be a large fluctuation of validation scores which are not completely positive correlated to the scores on testing set.
  • We have fixed the random seed in the updates. But there might still be some difference due to environment.
  • Multiple camera models in the testing phase may boost the performance by a little bit.

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Our solution to Domain Adaptive Pedestrian Re-identification in VisDA2020

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