This repo contains our code for VisDA2020 challenge at ECCV workshop.
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.
- Clone the repo
git clone https://github.com/vimar-gu/Bias-Eliminate-DA-ReID.git
- Install dependencies:
- pytorch >= 1.0.0
- python >= 3.5
- Prepare dataset. It can be obtained from Simon4Yan/VisDA2020.
- We use ResNet-ibn and HRNet as backbones. ImageNet pretrained models can be downloaded in here and here.
If you want to reproduce our results, please refer to [VisDA.md]
The performance on VisDA2020 validation dataset
|+ Domain Adaptation||44.9||75.3||86.7||91.0|
|+ Post Processing||70.9||86.5||92.8||94.4|
The models can be downloaded from:
- ResNet50-ibn-a: Google Drive
- ResNet101-ibn-a: Google Drive
- ResNet50-ibn-b: Google Drive
- HRNetv2-w18: Google Drive
- ResNet50-ibn-a-large: Google Drive
- ResNet101-ibn-a-large: Google Drive
- ResNet50-ibn-b-large: Google Drive
- HRNetv2-w18-large: Google Drive
The camera models can be downloaded from:
- Camera(ResNet101): Google Drive
- Camera(ResNet152): Google Drive
- Camera(ResNet101-ibn-a): Google Drive
- Camera(HRNetv2-w18): Google Drive
- 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.