VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera
This repo includes the 2nd place solution for AICity2020 Challenge ReID track. Our paper
Update
- In ECCV VisDA 2020 Person ReID challenge, all the top3 teams adopt camera bias post-process. It brings 5%-10% increment in challenge.
Introduction
Our work aims to eliminate the bias posed by similar background and shape
This project is mainly based on reid-strong-baseline and deep-person-reid
TODO
- FP16 training (30% faster with no precision drop)
- circle loss (borrowed from fast-reid)
- more metric learning methods (GeM, arcface, batch_soft)
- more backbones (OSNet, ResNest, RegNet)
- Experiments on Person-ReID
requirement
- pytorch>=1.2.0
- yacs
- apex (optional for FP16 training, if you don't have apex installed, please turn-off FP16 training by setting SOLVER.FP16=False)
$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
- python>=3.7
- cv2
Reproduce the result on AICity 2020 Challenge
Please see AICity2020.md for details.
Train
We use ResNet-ibn as backbone which proves to be quite robust on Person ReID. Download ImageNet pretrain model at here
- Vehicle ReID.
bash ./scripts/aicity20/train.sh
- Orientation ReID Train orientation ReID model
bash ./scripts/aicity20/ReOriID.sh
- Camera ReID Train camera ReID model
bash ./scripts/aicity20/ReCamID.sh
you can either download our trained models
Test
- generate orientation and camera similarity matrix
# ReCamID
python tools/test.py --config_file='configs/aicity20.yml' \
MODEL.DEVICE_ID "('1')" \
MODEL.NAME "('resnet50_ibn_a')" \
MODEL.MODEL_TYPE "baseline" \
DATASETS.TRAIN "('aicity20',)" \
DATASETS.TEST "('aicity20',)" \
DATASETS.ROOT_DIR "('/home/zxy/data/ReID/vehicle')" \
MODEL.PRETRAIN_CHOICE "('self')" \
TEST.WRITE_RESULT True \
TEST.WEIGHT "('./output/aicity20/0409-ensemble/ReCamID/best.pth')"
python ./tools/aicity20/compute_distmat_from_feats.py --src_dir ./output/aicity20/0409-ensemble/ReCamID/
# ReOriID
python tools/test.py --config_file='configs/aicity20.yml' \
MODEL.DEVICE_ID "('2')" \
MODEL.NAME "('resnet50_ibn_a')" \
MODEL.MODEL_TYPE "baseline" \
DATASETS.TRAIN "('aicity20',)" \
DATASETS.TEST "('aicity20',)" \
DATASETS.ROOT_DIR "('/home/zxy/data/ReID/vehicle')" \
MODEL.PRETRAIN_CHOICE "('self')" \
TEST.WRITE_RESULT True \
TEST.WEIGHT "('./output/aicity20/0409-ensemble/ReOriID/best.pth')"
python ./tools/aicity20/compute_distmat_from_feats.py --src_dir ./output/aicity20/0409-ensemble/ReOriID/
- Vehicle ReID based on orientation and camera
python tools/aicity20/submit.py --config_file='configs/aicity20.yml' \
MODEL.DEVICE_ID "('1')" \
MODEL.NAME "('resnet50_ibn_a')" \
MODEL.MODEL_TYPE "baseline" \
DATASETS.TRAIN "('aicity20',)" \
DATASETS.TEST "('aicity20',)" \
DATASETS.ROOT_DIR "('/home/zxy/data/ReID/vehicle')" \
MODEL.PRETRAIN_CHOICE "('self')" \
INPUT.SIZE_TRAIN '([320, 320])' \
INPUT.SIZE_TEST '([320, 320])' \
TEST.DO_RERANK True \
TEST.RERANK_PARAM "([50, 15, 0.5])" \
TEST.FLIP_TEST True \
TEST.WRITE_RESULT True \
TEST.USE_VOC True \
TEST.CAM_DIST_PATH './output/aicity20/0409-ensemble/ReCamID/feat_distmat.npy' \
TEST.ORI_DIST_PATH './output/aicity20/0409-ensemble/ReOriID/feat_distmat.npy' \
TEST.WEIGHT "('./output/aicity20/0409-ensemble/r50-320-circle/best.pth')"
Performance
Ablation study on AICity 2020 validation dataset
| method | mAP | Rank1 | comment |
|---|---|---|---|
| BagOfTricks | 21.6% | 42.1% | reid-strong-baseline |
| +Arcface | 26.2% | 46.7% | Arcface loss |
| +Circle | 29.7% | 50.8% | circle loss |
| +Syn | 39.5% | 64.0% | Syn denotes Synthetic dataset VehicleX |
| +WeaklyAug | 44.4% | 65.3% | WeaklyAug denotes weakly supervised crop augmentation |
| +Orientation | 47.0% | 70.5% | penalized by Orientation ReID |
| +Camera | 50.8% | 75.5% | penalized by Camera ReID |
AICITY2020 Challange Track2 Leaderboard
| TeamName | mAP | Link |
|---|---|---|
| Baidu-UTS | 84.1% | code |
| RuiYanAI(ours) | 78.1% | |
| DMT | 73.1% | code |
Results on VeRi
| method | mAP | Rank1 | comment |
|---|---|---|---|
| resnet50 | - | - | P=4,K=16,Size=[256, 256] |
| ResNet50_ibn_a | 81.6% | 96.8% | P=4,K=16,Size=[320, 320], log |
| +Orientation | 82.8% | 97.6% | distmat - 0.1 * ori_distmat, log |
| +Camera | - | - |
Failure cases that rectified by Orientation and Camera information
Citation
If you find our work helpful, please cite it
@inproceedings{
title={VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera},
author={Zhu, Xiangyu and Luo, Zhenbo and Fu, Pei and Ji, Xiang},
booktitle={Proc. CVPR Workshops},
year={2020}
}