  Object detection in densely packed scenes is a new area where standard object
detectors fail to train well. Dense object detectors like RetinaNet trained on
large and dense datasets show great performance. We train a standard object
detector on a small, normally packed dataset with data augmentation techniques.
This dataset is 265 times smaller than the standard dataset, in terms of number
of annotations. This low data baseline achieves satisfactory results (mAP=0.56)
at standard IoU of 0.5. We also create a varied benchmark for generic SKU
product detection by providing full annotations for multiple public datasets.
It can be accessed at
https://github.com/ParallelDots/generic-sku-detection-benchmark. We hope that
this benchmark helps in building robust detectors that perform reliably across
different settings in the wild.
