This repo is developed for Strong Baseline For Vehicle Re-Identification in Track 2 Ai-City-2021 Challenges

Overview

A STRONG BASELINE FOR VEHICLE RE-IDENTIFICATION

This paper is accepted to the IEEE Conference on Computer Vision and Pattern Recognition Workshop(CVPRW) 2021

This repo is the official implementation for the paper A Strong Baseline For Vehicle Re-Identification in Track 2, 2021 AI CITY CHALLENGE.

I.INTRODUCTION

Our proposed method sheds light on three main factors that contribute most to the performance, including:

  • Minizing the gap between real and synthetic data
  • Network modification by stacking multi heads with attention mechanism to backbone
  • Adaptive loss weight adjustment.

Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark.

II. INSTALLATION

  1. pytorch>=1.2.0
  2. yacs
  3. apex (optional for FP16 training, if you don't have apex installed, please turn-off FP16 training by setting SOLVER.FP16=False)
$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
  1. python>=3.7
  2. cv2

III. REPRODUCE THE RESULT ON AICITY 2020 CHALLENGE

Download the pretrained checkpoint resnext101_ibn

1.Train

  • Vehicle ReID
    ./scripts/train.sh
  • Orientation ReID
    ./scripts/ReOriID.sh
  • Camera ReID
    ./scripts/ReCamID.sh

2. Test

    ./scripts/test.sh

IV. PERFORMANCE

1. Comparison with state-of-the art methods on VeRi776

Owner
Cybercore Co. Ltd
Cybercore Co. Ltd
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