# Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance

arXiv: https://arxiv.org/abs/1810.06208 · HTML: https://mlsanity.com/paper/1810.06208/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues](https://mlsanity.com/paper/2402.14762/) (2024)
- [Distribution Alignment: A Unified Framework for Long-tail Visual Recognition](https://mlsanity.com/paper/2103.16370/) (2021)
- [Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fields](https://mlsanity.com/paper/2406.18311/) (2024)
- [Scale Normalized Image Pyramids with AutoFocus for Object Detection](https://mlsanity.com/paper/2102.05646/) (2021)
- [Dataset balancing can hurt model performance](https://mlsanity.com/paper/2307.00079/) (2023)
- [Real-Time Summarization of Twitter](https://mlsanity.com/paper/2407.08125/) (2024)
- [Web-Scale Generic Object Detection at Microsoft Bing](https://mlsanity.com/paper/2107.01814/) (2021)

## Abstract (arXiv)

This report demonstrates our solution for the Open Images 2018 Challenge. Based on our detailed analysis on the Open Images Datasets (OID), it is found that there are four typical features: large-scale, hierarchical tag system, severe annotation incompleteness and data imbalance. Considering these characteristics, an amount of strategies are employed, including SNIPER, soft sampling, class-aware sampling (CAS), hierarchical non-maximum suppression (HNMS) and so on. In virtue of these effective strategies, and further using the powerful SENet154 armed with feature pyramid module and deformable ROIalign as the backbone, our best single model could achieve a mAP of 56.9%. After a further ensemble with 9 models, the final mAP is boosted to 62.2% in the public leaderboard (ranked the 2nd place) and 58.6% in the private leaderboard (ranked the 3rd place, slightly inferior to the 1st place by only 0.04 point).