Retail Object Detection
The retail object detection model detects one or more items within an image and returns a bounding box around each detected item. These retail items are generally packaged commercial goods with barcodes and ingredient labels on them, as seen at a check-out counter.
As part of this model instance, 2 detectors are provided.
Binary class detector
The binary class detector, detects general retail items and returns a single category.
The 100 class retail detector on the other hand, detects one more objects belonging to 100 specific classes.
These models are based on EfficientDet-D5. EfficientDet is a one-stage detector with the following architecture components:
NvImageNet pretrained EfficientNet-B5 backbone
Weighted bi-directional feature pyramid network (BiFPN)
Bounding and classification box head
A compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time
The primary use case intended for these models is to detect object on a retail checkout counter.
The datasheet for this model is captured in it’s model card host at NGC_.