# Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection Source: [Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:14:59.273Z Authors: Srikrishna Varadarajan, Sonaal Kant, Muktabh Mayank Srivastava Published: 2019-12-19T18:57:45.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()