Update README.md

README.md

@@ -13,7 +13,10 @@ tags:
 - group
 - object-detection
 - sports
-dataset_summary: '
+- tracking
+- action-spotting
+- game-state-recognition
+dataset_summary: >
 
 
 
@@ -21,13 +24,14 @@ dataset_summary: '
 
 
 
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 1799 samples.
+  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 1799
+  samples.
 
 
   ## Installation
 
 
-  If you haven''t already, install FiftyOne:
+  If you haven't already, install FiftyOne:
 
 
   ```bash
@@ -49,9 +53,9 @@ 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("jamarks/SoccerNet-V3")
+  dataset = fouh.load_from_hub("Voxel51/SoccerNet-V3")
 
 
   # Launch the App
@@ -59,13 +63,13 @@ dataset_summary: '
   session = fo.launch_app(dataset)
 
   ```
-
-  '
 ---
 
 # Dataset Card for SoccerNet-V3
 
-<!-- Provide a quick summary of the dataset. -->
+SoccerNet is a large-scale dataset for soccer video understanding. It has evolved over the years to include various tasks such as action spotting,
+camera calibration, player re-identification and tracking. It is composed of 550 complete broadcast soccer games and 12 single camera games
+taken from the major European leagues. SoccerNet is not only dataset, but also yearly challenges where the best teams compete at the international level. 
 
 
 
@@ -91,7 +95,7 @@ import fiftyone.utils.huggingface as fouh
 
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = fouh.load_from_hub("jamarks/SoccerNet-V3")
+dataset = fouh.load_from_hub("Voxel51/SoccerNet-V3")
 
 # Launch the App
 session = fo.launch_app(dataset)
@@ -105,127 +109,96 @@ 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]
 - **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]
-
-## 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. -->
+- **Repository:** https://github.com/SoccerNet
+- **Paper** [SoccerNet 2023 Challenges Results](https://arxiv.org/abs/2309.06006)
+- **Demo:** https://try.fiftyone.ai/datasets/soccernet-v3/samples
+- **Homepage** https://www.soccer-net.org/
 
-[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]
+Dataset Authors:
 
-#### Who are the source data producers?
+Copyright (c) 2021 holders: 
 
-<!-- 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. -->
+- University of Liège (ULiège), Belgium.
+- King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
+- Marc Van Droogenbroeck (M.VanDroogenbroeck@uliege.be), Professor at the University of Liège (ULiège).
 
-[More Information Needed]
+Code Contributing Authors: 
 
-### Annotations [optional]
+- Anthony Cioppa (anthony.cioppa@uliege.be), University of Liège (ULiège), Montefiore Institute, TELIM.
+- Adrien Deliège (adrien.deliege@uliege.be), University of Liège (ULiège), Montefiore Institute, TELIM.
+- Silvio Giancola (silvio.giancola@kaust.edu.sa), King Abdullah University of Science and Technology (KAUST), Image and Video Understanding Laboratory (IVUL), part of the Visual Computing Center (VCC).
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+Supervision from:
 
-#### Annotation process
+- Bernard Ghanem, King Abdullah University of Science and Technology (KAUST).
+- Marc Van Droogenbroeck, University of Liège (ULiège).
 
-<!-- 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. -->
+### Funding
 
-[More Information Needed]
+Anthony Cioppa is funded by the FRIA, Belgium.
+This work is supported by the DeepSport and TRAIL projects of the Walloon Region, at the University of Liège (ULiège), Belgium.
+This work was supported by the Service Public de Wallonie (SPW) Recherche under the DeepSport project and Grant No.326 2010235 (ARIAC by https://DigitalWallonia4.ai)
+This work is also supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) (award327 OSR-CRG2017-3405).
 
-#### 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]
+## 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:**
 
-[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]
+```bibtex
+
+@inproceedings{Giancola_2018,
+   title={SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos},
+   url={http://dx.doi.org/10.1109/CVPRW.2018.00223},
+   DOI={10.1109/cvprw.2018.00223},
+   booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
+   publisher={IEEE},
+   author={Giancola, Silvio and Amine, Mohieddine and Dghaily, Tarek and Ghanem, Bernard},
+   year={2018},
+   month=jun }
+
+@misc{deliège2021soccernetv2,
+      title={SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos}, 
+      author={Adrien Deliège and Anthony Cioppa and Silvio Giancola and Meisam J. Seikavandi and Jacob V. Dueholm and Kamal Nasrollahi and Bernard Ghanem and Thomas B. Moeslund and Marc Van Droogenbroeck},
+      year={2021},
+      eprint={2011.13367},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@misc{cioppa2022soccernettracking,
+      title={SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos}, 
+      author={Anthony Cioppa and Silvio Giancola and Adrien Deliege and Le Kang and Xin Zhou and Zhiyu Cheng and Bernard Ghanem and Marc Van Droogenbroeck},
+      year={2022},
+      eprint={2204.06918},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@article{Cioppa2022,
+  title={Scaling up SoccerNet with multi-view spatial localization and re-identification},
+  author={Cioppa, Anthony and Deli{\`e}ge, Adrien and Giancola, Silvio and Ghanem, Bernard and Van Droogenbroeck, Marc},
+  journal={Scientific Data},
+  year={2022},
+  volume={9},
+  number={1},
+  pages={355},
+}
+```
 
-## Dataset Card Authors [optional]
 
-[More Information Needed]
 
-## Dataset Card Contact
+## Dataset Card Authors
 
-[More Information Needed]
+[Jacob Marks](https://huggingface.co/jamarks)