Awesome
Implementation of Vehicle Re-Identification Based on Complementary Features for 2020 AICity Challenge Track2
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
Dependencies
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
Datasets used in our implementation are avialable at
| Datasets | Description | Download link |
|---|---|---|
| Original train set | CityFlow train set and Simulation Set by VehicleX | link |
| Crop train set for cityflow | CityFlow train set cropped by 2019 1st Baidu's detector | link |
| All images with fake label test test | CityFlow train set & test set(fake label) and Simulation Set by VehicleX | link |
Models
Notes: You can directly use our trained model and extracted features for implementation, the link is as follows:
| Models | Usage | Description | Download link |
|---|---|---|---|
| ImageNet pretrained models | Train | for global_model/pretrain_models | link |
| ImageNet pretrained models | Train | for mgn_model/weights | link |
| Vehicle ReID models trained by us | Test | contain four models checkpoint mentioned in our paper | link |
| Features | Usage | Description | Download link |
|---|---|---|---|
| Pkls for features | Test | features extracted by each model and performed by several post-processing | link |
Code Structure
Each part has its own README file.
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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.
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mgn_mode contains source codes of training vehicle reid models including resnet152 with MGN, resnet152 with SAC.
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post_processing contains several post-processing methods for Re-ID task.
Running Code orderly
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Train each single model in global_model and mgn_model.
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Extract features for test set using each single model in global_model and mgn_model.
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Move all pkls of features to the same directory and utilize several post-processing methods to improve single model performence.
Detail steps to reproduce our result
Trainng
Train each single model in global_model and mgn_model.
see global_models/README.md and mgn_model/README.md for detailed training steps.
Testing
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
Basic framework
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The basic framework of our approach .
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Results generated by our method.
