Update README.md

README.md

@@ -12,7 +12,8 @@ tags:
 - fiftyone
 - image
 - image-classification
-dataset_summary: '
+- polylines
+dataset_summary: >
 
 
 
@@ -20,13 +21,14 @@ dataset_summary: '
 
 
 
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 15620 samples.
+  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 15620
+  samples.
 
 
   ## Installation
 
 
-  If you haven''t already, install FiftyOne:
+  If you haven't already, install FiftyOne:
 
 
   ```bash
@@ -48,23 +50,23 @@ dataset_summary: '
 
   # Load the dataset
 
-  # Note: other available arguments include ''max_samples'', etc
+  # Note: other available arguments include 'max_samples', etc
 
   dataset = fouh.load_from_hub("NeoKish/indoorCVPR_09")
 
+  # dataset = fouh.load_from_hub("NeoKish/indoorCVPR_09", max_samples=1000)
+
 
   # Launch the App
 
   session = fo.launch_app(dataset)
 
   ```
-
-  '
 ---
 
 # Dataset Card for indoorCVPR_09
 
-<!-- Provide a quick summary of the dataset. -->
+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. 
 
 
 
@@ -72,7 +74,7 @@ dataset_summary: '
 ![image/png](dataset_preview.jpg)
 
 
-This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 15620 samples.
+This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 15620 samples.  Dataset conversion to FiftyOne format and data card contributed by Kishan Savant
 
 ## Installation
 
@@ -103,128 +105,72 @@ session = fo.launch_app(dataset)
 
 <!-- Provide a longer summary of what this dataset is. -->
 
-
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
+- **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 [optional]
+### Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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. -->
 
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
+- categorizing indoor scenes and segmentation of the objects in a scene
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
+```plaintext
+Name:        indoorCVPR_09
+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
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
-
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
-
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+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. -->
 
-[More Information Needed]
-
-#### Who are the annotators?
+A subset of the images are segmented and annotated with the objects that they contain. The annotations are in LabelMe format
 
-<!-- This section describes the people or systems who created the annotations. -->
 
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
-
-## Dataset Card Contact
-
-[More Information Needed]
+```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}}
+```