{"url":"/dataset/mit-indoors-scenes","name":"MIT Indoor Scenes","full_name":null,"description_markdown":"Context\r\nThis is the Original data provided by MIT .\r\n\r\nIndoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information.\r\n\r\nContent\r\nThe database contains 67 Indoor categories, and a total of 15620 images. The number of images varies across categories, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only.\r\n\r\nAcknowledgements\r\nThanks to MIT\r\nThanks to Aude Oliva for helping to create the database of indoor scenes.\r\nFunding for this research was provided by NSF Career award (IIS 0747120)","description_withheld":null,"homepage":"https://www.kaggle.com/itsahmad/indoor-scenes-cvpr-2019","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Scene Recognition","url":"/task/scene-recognition","datasets_with_task":"/datasets/task/scene-recognition"}],"languages":[],"variants":["MIT Indoor Scenes"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/scene-recognition-on-mit-indoors-scenes","task":"Scene Recognition","dataset_variant":"MIT Indoor Scenes","rows":3,"metrics":["Accuracy","10-stage average accuracy"],"first_row_in_archive_order":{"model":"FOSNet","paper":"/paper/fosnet-an-end-to-end-trainable-deep-neural","metrics":{"Accuracy":"90.3"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/srrm-semantic-region-relation-model-for","title":"SRRM: Semantic Region Relation Model for Indoor Scene Recognition","date":"2023-05-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semantic-aware-scene-recognition","title":"Semantic-Aware Scene Recognition","date":"2019-09-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fosnet-an-end-to-end-trainable-deep-neural","title":"FOSNet: An End-to-End Trainable Deep Neural Network for Scene Recognition","date":"2019-07-17","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}