{
    "data_id": "45923",
    "name": "IndoorScenes",
    "exact_name": "IndoorScenes",
    "version": 1,
    "version_label": "v1",
    "description": "Indoor 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. The 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.",
    "format": "arff",
    "uploader": "Andrei Simion-Constantinescu",
    "uploader_id": 33923,
    "visibility": "public",
    "creator": "\"Massachusetts Institute of Technology\"",
    "contributor": null,
    "date": "2024-03-08 16:24:28",
    "update_comment": null,
    "last_update": "2024-03-08 16:24:28",
    "licence": "Public",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22117614\/dataset",
    "default_target_attribute": "Class_encoded",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "IndoorScenes",
            "Indoor 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 d "
        ],
        "weight": 5
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    "qualities": {
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        "NumberOfFeatures": 3,
        "NumberOfClasses": 67,
        "NumberOfMissingValues": 0,
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        "NumberOfNumericFeatures": 0,
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        "MinorityClassPercentage": 0.646606914212548,
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        "NumberOfBinaryFeatures": 0,
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        "AutoCorrelation": 1,
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        "Dimensionality": 0.00019206145966709347,
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}