A product detector based on the SSD-lite architecture with MobileNetV2 as a backbone for self-checkout points of sale-related scenes. The network can detect 12 classes of objects (sprite
, kool-aid
, extra
, ocelo
, finish
, mtn_dew
, best_foods
, gatorade
, heinz
, ruffles
, pringles
, del_monte
). Labels 0 and 1 are related to background_label
and undefined
correspondingly.
Metric | Value |
---|---|
Average Precision (AP) @[ IoU=0.50:0.95, area=all, maxDets=100 ] | 0.715 |
GFlops | 3.598 |
MParams | 3.212 |
Source framework | PyTorch* |
Name: input
, shape: [1x3x512x512]. An input image in the format [BxCxHxW], where:
Expected color order: BGR.
image_id
, label
, conf
, x_min
, y_min
, x_max
, y_max
] format, where:image_id
- ID of the image in the batchlabel
- predicted class IDconf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box cornerx_max
, y_max
) - coordinates of the bottom right bounding box corner[*] Other names and brands may be claimed as the property of others.