Use Case and High-Level Description

This is a person reidentification model for a general scenario. It uses a whole body image as an input and outputs an embedding vector to match a pair of images by the cosine distance. The model is based on the OmniScaleNet backbone with Linear Context Transform (LCT) blocks developed for fast inference. A single reidentification head from the 1/16 scale feature map outputs an embedding vector of 256 floats.



Metric Value
Market-1501 rank@1 accuracy 92.9 %
Market-1501 mAP 76.6 %
Pose coverage Standing upright, parallel to image plane
Support of occluded pedestrians YES
Occlusion coverage <50%
GFlops 0.564
MParams 0.595
Source framework PyTorch*

The cumulative matching curve (CMC) at rank-1 is accuracy denoting the possibility to locate at least one true positive in the top-1 rank. Mean Average Precision (mAP) is the mean across Average Precision (AP) of all queries. AP is defined as the area under the precision and recall curve.



The net expects one input image of the shape [1x3x256x128] in the [BxCxHxW] format, where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

The expected color order is BGR.


The net outputs a blob with the [1, 256] shape named descriptor, which can be compared with other descriptors using the cosine distance.

Legal Information

[*] Other names and brands may be claimed as the property of others.