Papers › A Solution to Product detection in Densely Packed Scenes
A Solution to Product detection in Densely Packed Scenes
Tianze Rong, Yanjia Zhu, Hongxiang Cai, Yichao Xiong
This work is a solution to densely packed scenes dataset SKU-110k. Our work is modified from Cascade R-CNN. To solve the problem, we proposed a random crop strategy to ensure both the sampling rate and input scale is relatively sufficient as a contrast to the regular random crop. And we adopted some of trick and optimized the hyper-parameters. To grasp the essential feature of the densely packed scenes, we analysis the stages of a detector and investigate the bottleneck which limits the performance. As a result, our method obtains 58.7 mAP on test set of SKU-110k.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Dense Object Detection | SKU-110K | Cascade-RCNN | AP | 58.7 | #2 of 5 | Archive leaderboard | report |
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Methods
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