Datasets:

Voxel51
/

LVIS

@@ -46,7 +46,7 @@ dataset_summary: '
   # Note: other available arguments include ''max_samples'', etc
-  dataset = fouh.load_from_hub("harpreetsahota/LVIS")
   # Launch the App
@@ -59,15 +59,13 @@ dataset_summary: '
 ---
 # Dataset Card for LVIS-35k
-<!-- Provide a quick summary of the dataset. -->
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples.
 ## Installation
 If you haven't already, install FiftyOne:
@@ -84,7 +82,7 @@ import fiftyone.utils.huggingface as fouh
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = fouh.load_from_hub("harpreetsahota/LVIS")
 # Launch the App
 session = fo.launch_app(dataset)
@@ -95,130 +93,40 @@ 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:** [More Information Needed]
-### 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. -->
-[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. -->
-#### 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
-<!-- 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. -->
 **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]

   # Note: other available arguments include ''max_samples'', etc
+  dataset = fouh.load_from_hub("Voxel51/LVIS")
   # Launch the App
 ---
 # Dataset Card for LVIS-35k
+![image](LVIS.gif)
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples.
+**NOTE:** This is only a 35k sample subset of the full dataset. The notebook recipe for creating this, and the full, dataset can be found [here](https://colab.research.google.com/drive/1SmdZPWtLhNis_cCRnO9WKKZQ9OaP_C_d)
 ## Installation
 If you haven't already, install FiftyOne:
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
+dataset = fouh.load_from_hub("Voxel51/LVIS")
 # Launch the App
 session = fo.launch_app(dataset)
 ### Dataset Description
+LVIS (pronounced 'el-vis') is a dataset for large vocabulary instance segmentation, introduced by researchers from Facebook AI.
+- It contains annotations for over 1000 object categories across 164k images. The full dataset is planned to have ~2 million high-quality instance segmentation masks.
+- The categories in LVIS follow a natural long-tail distribution, with a few common categories and many rare ones with few training examples. This long tail poses a challenge for current state-of-the-art object detection methods which struggle with low-sample categories.
+- The vocabulary was constructed iteratively, starting from 8.8k concrete noun synsets in WordNet and filtering down to the final set[4].
+- LVIS can be used for instance segmentation, semantic segmentation, and object detection tasks. The dataset aims to focus the research community on the open challenge of long-tail object recognition.
+In summary, LVIS is a large-scale, high-quality dataset that targets the difficult problem of learning segmentation models for various object categories, including many rare ones. It is freely available for research use.
+- **Curated by:** Agrim Gupta, Piotr Dollár, Ross Girshick
+- **Funded by:** Facebook AI Research (FAIR)
+- **Shared by:** [Harpreet Sahota](twitter.com/datascienceharp), Hacker-in-Residence at Voxel51
+- **Language(s) (NLP):** en
+- **License:** [Custom License](https://github.com/lvis-dataset/lvis-api/blob/master/LICENSE)
+### Dataset Sources [optional]
+- **Website:** https://www.lvisdataset.org/
+- **Repository:** https://github.com/lvis-dataset/lvis-api
+- **Paper:** https://arxiv.org/abs/1908.03195
+## Citation
 **BibTeX:**
+```bibtex
+@inproceedings{gupta2019lvis,
+  title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
+  author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
+  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
+  year={2019}
+}
+```