product-detection-0001#
Use Case and High-Level Description#
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.
Example#
Specification#
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* |
Inputs#
Image, name: input.1
, shape: 1, 3, 512, 512
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order: BGR
.
Outputs#
The net outputs a blob with shape: 1, 1, 200, 7
in the format 1, 1, N, 7
, where N
is the number of detected
bounding boxes. For each detection, the description has the [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 class(
x_min
,y_min
) - coordinates of the top left bounding box corner(
x_max
,y_max
) - coordinates of the bottom right bounding box corner
Demo usage#
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
Legal Information#
[*] Other names and brands may be claimed as the property of others.