Shortcuts

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

Docs

Access comprehensive documentation for Transfer Learning Library

View Docs

Tutorials

Get started for Transfer Learning Library

Get Started