vehicle_reid_itsc2023

Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification

Implementation of paper Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification

Version 1.0

All models are trained with CUDA 11.3 and PyTorch 1.11 on RTX4090 and Ryzen 7950X.

Resuls are displayed as mAP / CMC1 in percentage values %.

VehicleID was not available at the time of writing, we report values for VehicleID now. We follow evaluation as FastReid with 10-fold cross-validation to select queries and gallery.

To train:

python main.py --model_arch MBR_4G --config ./config/config_BoT_VERIWILD.yaml

Test:

python teste.py --path_weights ./logs/VERIWILD/MBR_4G/0/

If you want to test I share some of the weights.

After code reformultation there are some slight changes to the values from what is written on the paper.

Veri-776

Full Precision - Baseline mAP: 81.15 CMC1: 96.96 lambda:0.6 beta:1.0

R50	4G	2G	2X2G	2X	4X
CE + Tri	82.81/97.38	83.04/97.14	83.67/97.32	81.82/96.96	82.31/97.32
CE / Tri	82.47/96.84	83.26/97.02	84.22/97.02	83.67/97.50	83.89/97.5

R50+BoT	4G	2G	2X2G	2X	4X
CE + Tri	82.04/96.96	81.14/ 97.02	82.02/96.78	82.82/97.20	83.3/97.62
CE / Tri	82.67/97.02	NULL	82.57/97.32	NULL	84.72/97.68

Test with re_rank:

python teste.py --path_weights ./logs/Veri776/MBR_4B_LAI/0/ --re_rank

With re-ranking: mAP= 92.09, CMC1= 98.03, CMC5= 98.63

Veri-Wild

Half Precision - Baseline mAP: 87.24 CMC1: 96.65 lambda:0.8 beta:1.0 Some values were updated after detecting weird behaviours with nn.parallel usage.

R50	4G	2G	2X2G	2X	4X
CE + Tri	85.16/94.81	86.66/96.08	86.7/95.52	87.64/96.39	87.31/96.05
CE / Tri	84.05/93.41	86.11/95.28	86.91/95.78	87.31/95.98	87.73/96.02

R50+BoT	4G	2G	2X2G	2X	4X
CE + Tri	86.07/95.58	86.92/96.29	87.11/95.62	88.57/96.79	88.9/96.55
CE / Tri	85.29/94.85	NULL	86.52/95.21	NULL	86.9/95.75

VehicleID

Half Precision - Baseline mAP: 91.44 CMC1: 86.72 lambda:0.2 beta:1.0

R50	4G	2G	2X2G	2X	4X
CE + Tri	91.5/86.81	91.86/87.35	91.6/86.95	92.04/87.62	91.79/87.28
CE / Tri	90.68/85.56	90.94/85.59	91.44/86.66	91.45/86.83	91.91/87.36

Hybrid R50+BoT

R50+BoT	4G	2G	2X2G	2X	4X
CE + Tri	91.35/86.46	91.66/87.01	91.48/86.76	91.99/87.39	92.03/87.49
CE / Tri	90.36/85.17	NULL	91.15/86.22	NULL	92.75/88.46

Please cite our paper if inspired on proposed techniques or code.

@INPROCEEDINGS{10422175,
  author={Almeida, Eurico and Silva, Bruno and Batista, Jorge},
  booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title={Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification}, 
  year={2023},
  volume={},
  number={},
  pages={4690-4696},
  keywords={Representation learning;Metadata;Feature extraction;Real-time systems;Task analysis;Intelligent transportation systems;Residual neural networks},
  doi={10.1109/ITSC57777.2023.10422175}}

Some of the code is reused from:

Parsing-based View-aware Embedding Network for Vehicle Re-Identification

Bag of Tricks and A Strong Baseline for Deep Person Re-identification

FastREID

So please also cite and support their work.