re_identification.rst

Re-Identification

We provide benchmarks of different domain generalization algorithms. Currently three datasets are supported: Market1501, DukeMTMC, MSMT17. Those domain generalization algorithms includes:

• :ref:`IBN`
• :ref:`MIXSTYLE`

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