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naic_reid_challenge's Introduction

一、项目所需环境与运行方式

项目的文件结构

+-- tx_challenge
|   +-- train_set/
|   +-- query_a/
|   +-- gallery_a/
|   +-- query_b/
|   +-- gallery_b/
|   +-- train_list_new.txt
|   +-- val_gallery_list.txt
|   +-- val_query_list.txt

项目的运行步骤

  • git clone https://github.com/maliho0803/NAIC_reid_challenge.git
  • cd NAIC_reid_challenge

单模模型训练与推理

  1. 执行train.sh即可以开始训练
    • sh train.sh
  2. 执行validate.sh可以进行测试集rank1、map定量化评估，以及进行re-ranking参数进行自动化选择
    • sh validate.sh
  3. 执行test.sh，可直接生成大赛json文件
    • sh test.sh

单模型运行结果的位置

  • ./result/submission_example_A.json

多模型权重与对应测试集的距离矩阵信息

多模型融合提交结果的复现(多模型融合 生成提交结果)

  • python ensemble.py

运行结果的位置

  • 项目的根路径下生成 final_submit.json

二、数据和模型使用

预训练模型的使用情况

  • 仿MGN形式或者MGN魔改版的采用resnet50-ibn-a作为预训练的模型
  • DDA采用的是se-resnet101-ibn-a作为预训练的模型

相关论文及预训练模型的下载链接

三、项目运行环境

项目所需的工具包/框架

  • numpy: 1.16.4
  • tensorboardX: 1.9
  • pytorch: 1.2.0
  • tensorflow: 1.14.0
  • torchvision: 0.4.0
  • PIL: 6.1.0

项目运行的资源环境

  • 4卡11G 2080Ti

naic_reid_challenge's People

Contributors

maliho0803 avatar syncdevwu avatar

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