This repository contains the source codes of vehicle Re-ID of our implementation for 2020 AICity Challenge, and we got 5-th place in the vehicle Re-ID track of AIC2020. Our paper

python 2.7 / python 3.6

pytorch 1.0 +

torchvision 0.2.1 +

metric_learn 0.5.0 +

cv2 3.0 + refer to our source codes for other dependencies

Datasets used in our implementation are avialable at

Notes: You can directly use our trained model and extracted features for implementation, the link is as follows:

Each part has its own README file.

• global_model contains source codes of training vehicle reid models including se_resnext101, se_resnet152, resnet152, hrnet_48w, se_resnet152_ibnb, densenet161, dpn107, senet154 etc.

• mgn_mode contains source codes of training vehicle reid models including resnet152 with MGN, resnet152 with SAC.

• post_processing contains several post-processing methods for Re-ID task.

1. Train each single model in global_model and mgn_model.

2. Extract features for test set using each single model in global_model and mgn_model.

3. Move all pkls of features to the same directory and utilize several post-processing methods to improve single model performence.

Train each single model in global_model and mgn_model.

see global_models/README.md and mgn_model/README.md for detailed training steps.

1 Download the vehicle ReID models trained by us from the google-drive(link).

2 unzip the downloaded file ckpt.tar in the current directory, and make a link in both global_model and mgn_model directory

3 download imagenet pretrained models(link) for global_model/pretrain_models and unzip in global_mddel directory

4 download imagenet pretrained models(link) for mgn_model/weights and unzip in mgn_mddel directory

5 enter global_model directory, modify query_path and gallery_path in extract_feature_val.sh

6 extract features using global models, the features will be saved in post_processing/val_pkl_final

sh extract_feature_val.sh

7 enter mgn_model directory, modify query_path and gallery_path in extract_feature_val.sh

8 extract features using mgn model, the features will be saved in post_processing/val_pkl_final

sh extract_feature_val.sh

9 enter post_processing directory , run

sh test_final.sh

you will get track2.txt which is the final result

• The basic framework of our approach .
  

• Results generated by our method.