Datasets:

Voxel51
/

Describable-Textures-Dataset

@@ -50,7 +50,7 @@ dataset_summary: '
   # Note: other available arguments include ''max_samples'', etc
-  dataset = fouh.load_from_hub("jamarks/Describable-Textures-Dataset")
   # Launch the App
@@ -90,7 +90,7 @@ import fiftyone.utils.huggingface as fouh
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = fouh.load_from_hub("jamarks/Describable-Textures-Dataset")
 # Launch the App
 session = fo.launch_app(dataset)
@@ -101,130 +101,65 @@ session = fo.launch_app(dataset)
 ### Dataset Description
-<!-- 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]
-- **Language(s) (NLP):** en
-- **License:** other
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## 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]
-### 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]
 ## Dataset Creation
 ### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
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 ### 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. -->
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-#### 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. -->
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-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### 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. -->
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-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
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-#### 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. -->
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### 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. -->
 **BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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-## More Information [optional]
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-## Dataset Card Authors [optional]
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-## Dataset Card Contact
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   # Note: other available arguments include ''max_samples'', etc
+  dataset = fouh.load_from_hub("Voxel51/Describable-Textures-Dataset")
   # Launch the App
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
+dataset = fouh.load_from_hub("Voxel51/Describable-Textures-Dataset")
 # Launch the App
 session = fo.launch_app(dataset)
 ### Dataset Description
+"Our ability of vividly describing the content of images is a clear demonstration of the power of human visual system. Not only we can recognise objects in images (e.g. a cat, a person, or a car), but we can also describe them to the most minute details, extracting an impressive amount of information at a glance. But visual perception is not limited to the recognition and description of objects. Prior to high-level semantic understanding, most textural patterns elicit a rich array of visual impressions. We could describe a texture as "polka dotted, regular, sparse, with blue dots on a white background"; or as "noisy, line-like, and irregular".
+Our aim is to reproduce this capability in machines. Scientifically, the aim is to gain further insight in how textural information may be processed, analysed, and represented by an intelligent system. Compared to classic task of textural analysis such as material recognition, such perceptual properties are much richer in variety and structure, inviting new technical challenges.
+DTD is a texture database, consisting of 5640 images, organized according to a list of 47 terms (categories) inspired from human perception. There are 120 images for each category. Image sizes range between 300x300 and 640x640, and the images contain at least 90% of the surface representing the category attribute. The images were collected from Google and Flickr by entering our proposed attributes and related terms as search queries. The images were annotated using Amazon Mechanical Turk in several iterations. For each image we provide key attribute (main category) and a list of joint attributes.
+The data is split in three equal parts, in train, validation and test, 40 images per class, for each split. We provide the ground truth annotation for both key and joint attributes, as well as the 10 splits of the data we used for evaluation."
+- **Curated by:** M.Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, A. Vedaldi,
+- **Funded by:** NSF Grant #1005411, JHU-HLTCOE, Google Research, ERC grant VisRec no. 228180, ANR-10-JCJC-0205
+- **Language(s) (NLP):** en
+- **License:** other
+### Dataset Sources
+<!-- Provide the basic links for the dataset. -->
+- **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/dtd/
+- **Paper:** https://www.robots.ox.ac.uk/~vgg/publications/2014/Cimpoi14/cimpoi14.pdf
+- **Demo:** https://try.fiftyone.ai/datasets/describable-textures-dataset/samples
 ## Dataset Creation
 ### Curation Rationale
+'Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly.
+Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven
+texture terms and use them to describe a large dataset of
+patterns collected “in the wild”. The resulting Describable
+Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. ' - dataset authors
 ### Source Data
+Google and Flickr
+## Citation
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
+```bibtex
+@InProceedings{cimpoi14describing,
+	      Author    = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and and A. Vedaldi},
+	      Title     = {Describing Textures in the Wild},
+	      Booktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})},
+	      Year      = {2014}}
+```
+## More Information
+This research is based on work done at the 2012 CLSP Summer Workshop, and was partially supported by NSF Grant #1005411, ODNI via the JHU-HLTCOE and Google Research. Mircea Cimpoi was supported by the ERC grant VisRec no. 228180 and Iasonas Kokkinos by ANR-10-JCJC-0205.
+The development of the describable textures dataset started in June and July 2012 at the Johns Hopkins Centre for Language and Speech Processing (CLSP) Summer Workshop. The authors are most grateful to Prof. Sanjeev Khudanpur and Prof. Greg Hager.
+## Dataset Card Authors
+[Jacob Marks](https://huggingface.co/jamarks)