Black Re-ID: A Head-shoulder Descriptor for the Challenging Problem of Person Re-Identification
Training
To train a model, run
CUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <config.yml>
Evaluation
To evaluate the model in test set, run similarly:
CUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <configs.yaml> --eval-only MODEL.WEIGHTS model.pth
Experimental Results
Market1501 dataset
| Method |
Pretrained |
Rank@1 |
mAP |
| ResNet50 |
ImageNet |
93.3% |
84.6% |
| MGN |
ImageNet |
95.7% |
86.9% |
| HAA (ResNet50) |
ImageNet |
95% |
87.1% |
| HAA (MGN) |
ImageNet |
95.8% |
89.5% |
DukeMTMC dataset
| Method |
Pretrained |
Rank@1 |
mAP |
| ResNet50 |
ImageNet |
86.2% |
75.3% |
| MGN |
ImageNet |
88.7% |
78.4% |
| HAA (ResNet50) |
ImageNet |
87.7% |
75.7% |
| HAA (MGN) |
ImageNet |
89% |
80.4% |
Black-reid black group
| Method |
Pretrained |
Rank@1 |
mAP |
| ResNet50 |
ImageNet |
80.9% |
70.8% |
| MGN |
ImageNet |
86.7% |
79.1% |
| HAA (ResNet50) |
ImageNet |
86.7% |
79% |
| HAA (MGN) |
ImageNet |
91.0% |
83.8% |
White-reid white group
| Method |
Pretrained |
Rank@1 |
mAP |
| ResNet50 |
ImageNet |
89.5% |
75.8% |
| MGN |
ImageNet |
94.3% |
85.8% |
| HAA (ResNet50) |
ImageNet |
93.5% |
84.4% |
| HSE (MGN) |
ImageNet |
95.3% |
88.1% |
