{"url":"/dataset/fg-ovd","name":"FG-OVD","full_name":"Fine-Grained Open-Vocabulary object Detection benchmarks","description_markdown":"### Benchmark Suite Description for PapersWithCode\r\n\r\n**Fine-Grained Open-Vocabulary Detection (FG-OVD) Benchmark Suite**  \r\nThe FG-OVD benchmark suite evaluates the ability of open-vocabulary object detectors to discern fine-grained object properties such as color, material, pattern, and transparency. This suite introduces dynamic vocabularies for each object, consisting of one positive caption and several challenging negative captions, crafted using attribute substitution at varying difficulty levels. \r\n\r\nKey features include:  \r\n- **Difficulty-Based Benchmarks**: Trivial, Easy, Medium, and Hard benchmarks challenge detectors with progressively harder negative examples.  \r\n- **Attribute-Based Benchmarks**: Focused evaluation of specific attributes, such as color or material, with negative captions differing only in the targeted attribute.  \r\n- **Metrics**: Mean Average Precision (mAP) and Median Rank are used to measure both localization accuracy and fine-grained caption assignment performance.\r\n\r\nThe suite provides a comprehensive analysis of state-of-the-art models, highlighting their strengths and limitations in fine-grained object recognition.","description_withheld":null,"homepage":"https://github.com/lorebianchi98/FG-OVD/tree/main/benchmarks","introduced_date":"2023-11-29","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-devil-is-in-the-fine-grained-details","title":"The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding","first_author":"Lorenzo Bianchi","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["FG-OVD"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}