#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/IndoorSceneRecognition" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# 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.

![image/png](https://huggingface.co/datasets/Voxel51/IndoorSceneRecognition/resolve/main/dataset_preview.jpg)

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 15620 samples.

## Installation

If you haven’t already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
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

<!-- Provide a longer summary of what this dataset is. -->
- **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

<!-- Provide the basic links for the dataset. -->
- **Paper :** https://ieeexplore.ieee.org/document/5206537
- **Homepage:** https://web.mit.edu/torralba/www/indoor.html

## Uses

<!-- Address questions around how the dataset is intended to be used. -->
- categorizing indoor scenes and segmentation of the objects in a scene

## Dataset Structure

```plaintext
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

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

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

## Citation

**BibTeX:**

```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](https://huggingface.co/NeoKish)