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.

## 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.