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 |
|---|---|
| Pairwise accuracy | 92.11% |
| Pose coverage | Standing upright, parallel to image plane |
| Support of occluded pedestrians | YES |
| Occlusion coverage | <50% |
| GFlops | 0.03 |
| MParams | 0.28 |
| Source framework | Caffe* |
Pairwise accuracy is defined as the accuracy of the binary classification problem, where a positive class means that a pair of images represents the same person. Validation dataset consists of 10,000 image pairs of about 1500 persons.
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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.
- 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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