person-reidentification-retail-0288

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 developed for fast inference. A single reidentification head from the 1/16 scale feature map outputs an embedding vector of 256 floats.

Example

Specification

Metric Value
Market-1501 rank@1 accuracy 86.1 %
Market-1501 mAP 59.7 %
Pose coverage Standing upright, parallel to image plane
Support of occluded pedestrians YES
Occlusion coverage <50%
GFlops 0.174
MParams 0.183
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.

Inputs

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.

Outputs

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.