Update README.md

README.md

@@ -64,9 +64,10 @@ dataset_summary: '
 
 # Dataset Card for bdd100k-validation
 
-<!-- Provide a quick summary of the dataset. -->
-
 
+From one of the largest open source driving datasets, [BDD100k](https://www.vis.xyz/bdd100k/), is the BDD100K images dataset.
+The dataset consists of every 10th second in the videos and contains a train, validation and test split.
+It contains labels for object detection, weather, time of day, and scene of the driving!
 
 
 ![image/png](dataset_preview.gif)
@@ -121,110 +122,57 @@ session = fo.launch_app(dataset)
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
+By downoading, you are agreeing to the [BDD100K License](https://doc.bdd100k.com/license.html#license). 
 
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
+This dataset is great for self driving car applications, especially for dealing with many different weather conditions and different times of day!
 
 [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
+```
+Name:        bdd100k-validation
+Media type:  image
+Num samples: 10000
+Persistent:  True
+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)
+    weather:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
+    timeofday:  fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
+    scene:      fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
+    detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
+```
+    
 
-<!-- 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. -->
 
-[More Information Needed]
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+The dataset is sourced from [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://arxiv.org/abs/1805.04687)
 
-#### 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. -->
-
-#### 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?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
-
-#### Personal and Sensitive Information
+BDD100K is now managed by the [ETH VIS Group](https://www.vis.xyz/bdd100k/)
 
-<!-- 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]
+@misc{yu2020bdd100k,
+      title={BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning}, 
+      author={Fisher Yu and Haofeng Chen and Xin Wang and Wenqi Xian and Yingying Chen and Fangchen Liu and Vashisht Madhavan and Trevor Darrell},
+      year={2020},
+      eprint={1805.04687},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}