Re-Identification¶
We provide benchmarks of different domain generalization algorithms. Currently three datasets are supported: Market1501, DukeMTMC, MSMT17. Those domain generalization algorithms includes:
Note
We adopt cross dataset setting (another one is cross camera setting). The model is first trained on source dataset, then we evaluate it on target dataset and report mAP (mean average precision) on target dataset.
Note
For a fair comparison, our model is trained with standard cross entropy loss and triplet loss. We adopt modified resnet architecture from Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification (ICLR 2020).
Cross dataset mAP on ResNet-50¶
Methods |
Avg |
Market2Duke |
Duke2Market |
Market2MSMT |
MSMT2Market |
Duke2MSMT |
MSMT2Duke |
Baseline |
23.5 |
25.6 |
29.6 |
6.3 |
31.7 |
10.1 |
37.8 |
IBN |
27.0 |
31.5 |
33.3 |
10.4 |
33.6 |
13.7 |
40.0 |
MixStyle |
25.5 |
27.2 |
31.6 |
8.2 |
33.9 |
12.4 |
39.9 |
