Re-ID模型评估结果
预训练Resnet-18模型直接用于人车对
Dataset |
Rank-1 |
Rank-5 |
Rank-10 |
mAP |
mINP |
metric |
BikePerson |
0.12 |
0.65 |
1.22 |
0.19 |
0.09 |
0.15 |
VehicleID模型直接用于人车对
Dataset |
Rank-1 |
Rank-5 |
Rank-10 |
mAP |
mINP |
metric |
BikePerson |
0.97 |
2.47 |
3.71 |
0.60 |
0.13 |
0.78 |
Market1501 People Re-ID模型直接用于人车对
Dataset |
Rank-1 |
Rank-5 |
Rank-10 |
mAP |
mINP |
metric |
BikePerson |
0.93 |
2.55 |
3.80 |
0.62 |
0.13 |
0.77 |
Market1501 People Re-ID模型训练后用于人车对
Dataset |
Rank-1 |
Rank-5 |
Rank-10 |
mAP |
mINP |
metric |
BikePerson |
67.95 |
81.73 |
88.68 |
66.71 |
51.75 |
67.33 |
Market1501 People Re-ID模型训练后用于自行车
Dataset |
Rank-1 |
Rank-5 |
Rank-10 |
mAP |
mINP |
metric |
BikePerson |
56.25 |
71.43 |
79.53 |
52.79 |
34.39 |
54.52 |