mlsanityData to 7 Oct 2026

BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training

arXiv 2203.13249 ↗First posted 24 Mar 2022cs.CV cs.AIcited by 33

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Abstract (arXiv)

Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we construct a new large-scale benchmark termed BigDetection. Our goal is to simply leverage the training data from existing datasets (LVIS, OpenImages and Object365) with carefully designed principles, and curate a larger dataset for improved detector pre-training. Specifically, we generate a new taxonomy which unifies the heterogeneous label spaces from different sources. Our BigDetection dataset has 600 object categories and contains over 3.4M training images with 36M bounding boxes. It is much larger in multiple dimensions than previous benchmarks, which offers both opportunities and challenges. Extensive experiments demonstrate its validity as a new benchmark for evaluating different object detection methods, and its effectiveness as a pre-training dataset.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 62 of its 74 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

62 of 74 references.

References · 62

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Citing papers per year: 2022: 8; 2023: 13; 2024: 10; 2025: 1; 2026: 1.

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