For the leaderboard on public benchmarks, please refer to LEADERBOARD.md.
Note: all the models below are selected by the performance on validation sets.
Direct infer
models are directly tested on the re-ID datasets with ImageNet pre-trained weights.
Method | Backbone | Pre-trained | mAP(%) | top-1(%) | top-5(%) | top-10(%) | Train time | Download |
---|---|---|---|---|---|---|---|---|
Direct infer | ResNet50 | ImageNet | 2.2 | 6.7 | 14.9 | 20.1 | n/a | |
UDA_TP | ResNet50 | ImageNet | 34.7 | 58.6 | 74.0 | 78.9 | ~2h | [config] [model] |
strong_baseline | ResNet50 | ImageNet | 70.5 | 87.9 | 95.7 | 97.1 | ~2.5h | [config] [model] |
MMT | ResNet50 | ImageNet | 74.3 | 88.1 | 96.0 | 97.5 | ~4.5h | [config] [model] |
SpCL | ResNet50 | ImageNet | 76.0 | 89.5 | 96.2 | 97.5 | ~2h | [config] [model] |
Method | Backbone | Pre-trained | mAP(%) | top-1(%) | top-5(%) | top-10(%) | Train time | Download |
---|---|---|---|---|---|---|---|---|
Direct infer | ResNet50 | ImageNet | 2.3 | 7.5 | 14.7 | 18.1 | n/a | |
UDA_TP | ResNet50 | ImageNet | 42.3 | 64.4 | 76.0 | 79.9 | ~2h | [config] [model] |
strong_baseline | ResNet50 | ImageNet | 54.7 | 72.9 | 83.5 | 87.2 | ~2.5h | [config] [model] |
MMT | ResNet50 | ImageNet | 60.3 | 75.6 | 86.0 | 89.2 | ~4.5h | [config] [model] |
SpCL | ResNet50 | ImageNet | 67.1 | 82.4 | 90.8 | 93.0 | ~2h | [config] [model] |
Direct infer
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.
Method | Backbone | Pre-trained | mAP(%) | top-1(%) | top-5(%) | top-10(%) | Train time | Download |
---|---|---|---|---|---|---|---|---|
Direct infer | ResNet50 | DukeMTMC | 27.2 | 58.9 | 75.7 | 81.4 | ~1h | [config] [model] |
UDA_TP | ResNet50 | DukeMTMC | 52.3 | 76.0 | 87.8 | 91.9 | ~2h | [config] [model] |
strong_baseline | ResNet50 | ImageNet | 75.6 | 90.9 | 96.6 | 97.8 | ~3h | [config] [model] |
MMT | ResNet50 | ImageNet | 80.9 | 92.2 | 97.6 | 98.4 | ~6h | [config] [model] |
SpCL | ResNet50 | ImageNet | 78.2 | 90.5 | 96.6 | 97.8 | ~3h | [config] [model] |
Method | Backbone | Pre-trained | mAP(%) | top-1(%) | top-5(%) | top-10(%) | Train time | Download |
---|---|---|---|---|---|---|---|---|
Direct infer | ResNet50 | Market | 28.1 | 49.3 | 64.3 | 69.7 | ~1h | [config] [model] |
UDA_TP | ResNet50 | Market | 45.7 | 65.5 | 78.0 | 81.7 | ~2h | [config] [model] |
strong_baseline | ResNet50 | ImageNet | 60.4 | 75.9 | 86.2 | 89.8 | ~3h | [config] [model] |
MMT | ResNet50 | ImageNet | 67.7 | 80.3 | 89.9 | 92.9 | ~6h | [config] [model] |
SpCL | ResNet50 | ImageNet | 70.4 | 83.8 | 91.2 | 93.4 | ~3h | [config] [model] |