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 OSNet 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 94.8 %
Market-1501 mAP 83.7 %
Pose coverage Standing upright, parallel to image plane
Support of occluded pedestrians YES
Occlusion coverage <50%
GFlops 1.170
MParams 1.234
Source framework PyTorch*



The net expects one input image of the shape 1, 3, 256, 128 in the B, C, H, W format, where:

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

The expected color order is BGR.


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

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