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




Metric Value
Market-1501 rank@1 accuracy 0.7791
Market-1501 mAP 0.6180
Pose coverage Standing upright, parallel to image plane
Support of occluded pedestrians YES
Occlusion coverage <50%
GFlops 0.028
MParams 0.280
Source framework Caffe*

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 all queries’ Average Precision (AP) and AP is defined as an area under the precision/recall curve.


Link to performance table


  1. name: "data" , shape: [1x3x96x48] - An input image in the format [BxCxHxW], 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 shape: [1, 256, 1, 1] named descriptor, which can be compared with other descriptors using the Cosine distance.

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