Open-domain real-world entity recognition is essential yet challenging,
involving identifying various entities in diverse environments. The lack of a
suitable evaluation dataset has been a major obstacle in this field due to the
vast number of entities and the extensive human effort required for data
curation. We introduce Entity6K, a comprehensive dataset for real-world entity
recognition, featuring 5,700 entities across 26 categories, each supported by 5
human-verified images with annotations. Entity6K offers a diverse range of
entity names and categorizations, addressing a gap in existing datasets. We
conducted benchmarks with existing models on tasks like image captioning,
object detection, zero-shot classification, and dense captioning to demonstrate
Entity6K's effectiveness in evaluating models' entity recognition capabilities.
We believe Entity6K will be a valuable resource for advancing accurate entity
recognition in open-domain settings.

开放域现实世界实体识别的评估数据集 Entity6K 对于评估模型的实体识别能力具有重要的作用。