# person-reidentification-retail-0287¶

## 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.

## Specification¶

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.

## 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.