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Object detection (OD) in computer vision has made significant progress inrecent years, transitioning from closed-set labels to open-vocabulary detection(OVD) based on large-scale vision-language pre-training (VLP). However, currentevaluation methods and datasets are limited to testing generalization overobject types and referral expressions, which do not provide a systematic,fine-grained, and accurate benchmark of OVD models' abilities. In this paper,we propose a new benchmark named OVDEval, which includes 9 sub-tasks andintroduces evaluations on commonsense knowledge, attribute understanding,position understanding, object relation comprehension, and more. The dataset ismeticulously created to provide hard negatives that challenge models' trueunderstanding of visual and linguistic input. Additionally, we identify aproblem with the popular Average Precision (AP) metric when benchmarking modelson these fine-grained label datasets and propose a new metric calledNon-Maximum Suppression Average Precision (NMS-AP) to address this issue.Extensive experimental results show that existing top OVD models all fail onthe new tasks except for simple object types, demonstrating the value of theproposed dataset in pinpointing the weakness of current OVD models and guidingfuture research. Furthermore, the proposed NMS-AP metric is verified byexperiments to provide a much more truthful evaluation of OVD models, whereastraditional AP metrics yield deceptive results. Data is available at\url{https://github.com/om-ai-lab/OVDEval}

How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection