Benchmark for Generic Product Detection: A strong baseline for Dense Object Detection

Object detection in densely packed scenes is a new area where standard object detectors fail to train well (Goldman et al., 2019). We show that the performance of the standard object detectors on densely packed scenes is superior when it is trained on normal scenes rather than dense scenes. We train a standard object detector on a small, normally packed dataset with data augmentation techniques. This achieves significantly better results than state-of-the-art methods that are trained on densely packed scenes. We obtain 68.5% mAP on SKU110K dataset (Goldman et al., 2019), 19.3% higher and 1.4x better than the previous state-of-the-art. We also create a varied benchmark for generic SKU product detection by providing full annotations for multiple public datasets. It can be accessed at this [URL](https://github.com/ParallelDots/generic-sku-detection-benchmark). We hope that this benchmark helps in building robust detectors that perform reliably across different settings.