Note

This is a Hugging Face dataset. Learn how to load datasets from the Hub in the Hugging Face integration docs.

Hugging Face

Dataset Card for IndoorSceneRecognition#

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.

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This is a FiftyOne dataset with 15620 samples.

Installation#

If you haven’t already, install FiftyOne:

pip install -U fiftyone

Usage#

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/IndoorSceneRecognition")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details#

Dataset Description#

  • Curated by: A. Quattoni, A. Torralba, Aude Oliva

  • Funded by: National Science Foundation Career award (IIS 0747120)

  • Language(s) (NLP): en

  • License: mit

Dataset Sources#

  • Paper : https://ieeexplore.ieee.org/document/5206537

  • Homepage: https://web.mit.edu/torralba/www/indoor.html

Uses#

  • categorizing indoor scenes and segmentation of the objects in a scene

Dataset Structure#

Name:        IndoorSceneRecognition
Media type:  image
Num samples: 15620
Persistent:  False
Tags:        []
Sample fields:
    id:                     fiftyone.core.fields.ObjectIdField
    filepath:               fiftyone.core.fields.StringField
    tags:                   fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:               fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
    ground_truth:           fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    ground_truth_polylines: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Polylines)

The dataset has 3 splits: “train”, “val”, and “test”. Samples are tagged with their split.

Dataset Creation#

Curation Rationale#

The authors of the paper A. Quattoni and A.Torralba wanted to propose a prototype based model that can exploit local and global discriminative information in a indoor scene recognition problem. To test out the approach, with the help of Aude Oliva, they created a dataset of 67 indoor scenes categories covering a wide range of domains.

Annotation process#

A subset of the images are segmented and annotated with the objects that they contain. The annotations are in LabelMe format

Citation#

BibTeX:

@INPROCEEDINGS{5206537,
  author={Quattoni, Ariadna and Torralba, Antonio},
  booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition}, 
  title={Recognizing indoor scenes}, 
  year={2009},
  volume={},
  number={},
  pages={413-420},
  keywords={Layout},
  doi={10.1109/CVPR.2009.5206537}}

Dataset Card Authors#

Kishan Savant