Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
n<1K
License:
| annotations_creators: [] | |
| language: en | |
| license: mit | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - image-classification | |
| task_ids: [] | |
| pretty_name: IndoorSceneRecognition | |
| tags: | |
| - fiftyone | |
| - image | |
| - image-classification | |
| - CVPR2009 | |
| dataset_summary: > | |
|  | |
| 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") | |
| # dataset = fouh.load_from_hub("Voxel51/IndoorSceneRecognition", max_samples=1000) | |
| # Launch the App | |
| session = fo.launch_app(dataset) | |
| ``` | |
| # 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. | |
|  | |
| 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) | |