Cross-domain Person Re-Identification
Introduction
UDAStrongBaseline is a transitional code based pyTorch framework for both unsupervised learning (USL)
and unsupervised domain adaptation (UDA) in the object re-ID tasks. It provides stronger
baselines on these tasks. It needs the enviorment: Python >=3.6 and PyTorch >=1.1. We will transfer all the codes to the fastreid in the future (ongoing) from UDAStrongBaseline.
Unsupervised domain adaptation (UDA) on Person re-ID
Direct Transfer models are trained on the source-domain datasets
(source_pretrain) and directly tested on the target-domain datasets.- UDA methods (
MMT, SpCL, etc.) starting from ImageNet means that they are trained end-to-end
in only one stage without source-domain pre-training. MLT denotes to the implementation of our NeurIPS-2020.
Please note that it is a pre-released repository for the anonymous review process, and the official
repository will be released upon the paper published.
DukeMTMC-reID -> Market-1501
| Method |
Backbone |
Pre-trained |
mAP(%) |
top-1(%) |
top-5(%) |
top-10(%) |
Train time |
| Direct Transfer |
ResNet50 |
DukeMTMC |
32.2 |
64.9 |
78.7 |
83.4 |
~1h |
| UDA_TP PR'2020 |
ResNet50 |
DukeMTMC |
52.3 |
76.0 |
87.8 |
91.9 |
~2h |
| MMT ICLR'2020 |
ResNet50 |
DukeMTMC |
80.9 |
92.2 |
97.6 |
98.4 |
~6h |
| SpCL NIPS'2020 submission |
ResNet50 |
DukeMTMC |
78.2 |
90.5 |
96.6 |
97.8 |
~3h |
| strong_baseline |
ResNet50 |
DukeMTMC |
75.6 |
90.9 |
96.6 |
97.8 |
~3h |
| Our stronger_baseline |
ResNet50 |
DukeMTMC |
78.0 |
91.0 |
96.4 |
97.7 |
~3h |
| [MLT] NeurIPS'2020 submission |
ResNet50 |
DukeMTMC |
81.5 |
92.8 |
96.8 |
97.9 |
~ |
Market-1501 -> DukeMTMC-reID
| Method |
Backbone |
Pre-trained |
mAP(%) |
top-1(%) |
top-5(%) |
top-10(%) |
Train time |
| Direct Transfer |
ResNet50 |
Market |
34.1 |
51.3 |
65.3 |
71.7 |
~1h |
| UDA_TP PR'2020 |
ResNet50 |
Market |
45.7 |
65.5 |
78.0 |
81.7 |
~2h |
| MMT ICLR'2020 |
ResNet50 |
Market |
67.7 |
80.3 |
89.9 |
92.9 |
~6h |
| SpCL NIPS'2020 submission |
ResNet50 |
Market |
70.4 |
83.8 |
91.2 |
93.4 |
~3h |
| strong_baseline |
ResNet50 |
Market |
60.4 |
75.9 |
86.2 |
89.8 |
~3h |
| Our stronger_baseline |
ResNet50 |
Market |
66.7 |
80.0 |
89.2 |
92.2 |
~3h |
| [MLT] NeurIPS'2020 submission |
ResNet50 |
Market |
71.2 |
83.9 |
91.5 |
93.2 |
~ |
Market1501 -> MSMT17
| Method |
Source |
Rank@1 |
mAP |
mINP |
| DirectTransfer(R50) |
Market1501 |
29.8% |
10.3% |
9.3% |
| Our method |
DukeMTMC |
56.6% |
26.5% |
- |
DukeMTMC -> MSMT17
| Method |
Source |
Rank@1 |
mAP |
mINP |
| DirectTransfer(R50) |
DukeMTMC |
34.8% |
12.5% |
0.3% |
| Our method |
DukeMTMC |
59.5% |
27.7% |
- |