In AIcity2020 Track2, we rank number 6(team ID 44) among all the teams with the mAP of 0.6683 on server.

Chaoran Zhuge, Yujie Peng, Yadong Li, Jiangbo Ai, Junru Chen

our origin results on a deep learning framework without open source:

We have already reproduced our method on pytorch.The final result is a little lower than our origin results,which may caused by the difference of deep learning frameworks.

1.pytorch==1.4.0
2.torchvision==0.5.0
3.python==3.6
4.opencv-python==4.1.1.264
5.apex==0.1

if you use python3.7,please use the follwing orders to install apex:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

if you use python 3.6,please use the follwing orders to install apex:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir ./

Using apex to train can reduce video memory usage by 25% -30%,and the trained model has the same performance as not using apex.

If you want to reproduce our imagenet pretrained models,you need download ILSVRC2012 dataset,and make sure the folder architecture as follows:

ILSVRC2012
|
|-----train----1000 sub classes folders
|
|-----val----1000 sub classes folders
Please make sure the same class has same class folder name in train and val folders.

Or,you can use our pretrained models from here: https://drive.google.com/open?id=1nXHXbrmWuOCzDsMumWoXy9Atv60G0Guy.

If you want to reproduce our aicity track2 result,you need download AIC20_ReID dataset and AIC20_ReID_Simulation dataset,and make sure the folder architecture as follows:

AIC20_track2
|
|                |----image_train
|----AIC20_ReID--|----image_query
|                |----image_test
|
|----AIC20_ReID_Simulation----image_train

Also,you need download our dataset pkls to train and test.You can download it from here: https://drive.google.com/open?id=1zMvJhIl05X6goGu9SvxaXWEkMt99LDsF. Unzip it,you will get track2_train_pytorch.pkl,track2_simu_train_pytorch.pkl and benchmark_pytorch.pkl.

all images information in AIC20_ReID/image_train/.

track2_train_pytorch.pkl is a python list,each element is a python dictionary for a image.
keys of each dictionary item:
'vehicle_id'
'camera_id'
'bbox':predicted by a object detection model trained on COCO
'mask':predicted by a object segmentation model trained on COCO
'color':manually annotated vehicle color attributes
'type':manually annotated vehicle type attributes
'image_path':image relative path

all images information in AIC20_ReID_Simulation/image_train/.

track2_simu_train_pytorch.pkl is a python list, each element is a python dictionary for a image.
keys of each dictionary item:
'vehicle_id'
'camera_id'
'bbox':predicted by a object detection model trained on COCO
'mask':predicted by a object segmentation model trained on COCO
'color':manually annotated vehicle color attributes
'type':manually annotated vehicle type attributes
'image_path':image relative path

all data information in AIC20_ReID/image_query/ and AIC20_ReID/image_test/.

track2_simu_train_pytorch.pkl is a python dictionary which has two key:'reid_query' and 'reid_gallery', each key's value is a python list of corresponding sets.
'reid_query' value has all images information in AIC20_ReID/image_query/, and 'reid_gallery' has all images information in AIC20_ReID/image_test/.
each image key:
'vehicle_id'
'track_id':According to AIC20_ReID/test_track_id.txt,We numbered track_id for reid_gallery images,this track id is different from track_id in training set.
'bbox':predicted by a object detection model trained on COCO
'pred_color':predicted by a color classification model trained on AIC20_ReID/image_train/
'pred_type':redicted by a type classification model trained on AIC20_ReID/image_train/
'pred_color_prob':color prediction prob.
'pred_type_prob':type prediction prob.
'image_path':image relative path

We use a densenet161 model pretrained on imagenet with input size 320x320 and a resnet50+last_stride+ibn with input size 320x320. our pretrained models performance:

If you want to reproduce our pretrained models.You can find the training code in imagenet_experiments folder. For example,if you want to reproduce resnet50+last_stride+ibn,just enter imagenet_experiments/resnet50_320_ibn,then run:

./train.sh

our training needs 8 2080ti gpus.

In vehicle_reid_experiments,we trained five feature extractors and ensemble all feature extractors dists and features to get the final ensemble result. You can find each feature extractor training code in vehicle_reid_experiments folder.In our training code,We test per 2 epoch model after epoch 60 and save the best model, dist,features,txt for each feature extractor. For example,if you want to reproduce our res50 feature extractor,just enter vehicle_reid_experiments/res50,and run:

./train.sh

Please make sure you can load the pretrained models and our dataset_pkls correctly.

For each feature extractor training code,you can get a best model and related dist pkl, query features pkl,gallery features pkl,and txt for server commit. You can find Each feature extractor best model performance at the end of training_folder/log/main.info.log.Just like:

finish training, best_map: 0.xxxx, best_top1:0.xxxx

This result are local result eval from our val function,same rules as server.But for some unknown reason,the results always higher 1.0%-1.3% map than server result. If you want to get ensemble results,just enter public/reid/,and run:

./val_ensemble1.sh

Please ensure that the order of each pkl in dists/query features/gallery features are same.

If you find our work useful in your research, please consider citing:

@inproceedings{zgcr,
 title={Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification},
 author={Chaoran Zhuge, Yujie Peng, Yadong Li, Jiangbo Ai, Junru Chen},
 booktitle={AI City Challenge 2020 CVPR Workshop},
 year={2020}
}