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 # ⚽ SoccerNet
 
-### Large-scale datasets and benchmarks for soccer video understanding
+### Soccer Video Understanding Benchmark Suite
+
+**Datasets · Benchmarks · Challenges · Research**
 
-[🌐 Website](https://www.soccer-net.org) •
-[🤗 Datasets](https://huggingface.co/SoccerNet) •
-[💻 GitHub](https://github.com/SoccerNet) •
+[🌐 Website](https://www.soccer-net.org) ·
+[🤗 Datasets](https://huggingface.co/SoccerNet/datasets) ·
+[💻 GitHub](https://github.com/SoccerNet) ·
 [💬 Discord](https://discord.gg/cPbqf2mAwF)
 
-</div>
 
 ---
 
-## About SoccerNet
+## ⚽ What is SoccerNet?
+
+**SoccerNet** is a large-scale benchmark suite for **soccer video understanding**.
 
-**SoccerNet** is a large-scale benchmark suite for **soccer video understanding**, providing datasets, annotations, benchmarks, and yearly challenges to advance research in sports artificial intelligence.
+Started in 2018 with action spotting in broadcast soccer games, SoccerNet has progressively expanded toward a broad range of problems in video understanding, field understanding, player understanding, and game understanding.
 
-SoccerNet covers a broad range of computer vision and multimodal tasks, including:
+The data currently includes:
 
-- ⚡ Action Spotting & Anticipation
-- ⚽ Ball Action Spotting
-- 🎥 Camera Calibration & Understanding
-- 👥 Player Detection, Tracking & Re-Identification
-- 🗺️ Game State Reconstruction
-- 🥅 Multi-View Foul Recognition
-- 📏 Monocular Depth Estimation
-- 🧠 Visual Question Answering
-- 🎬 Novel View Synthesis
-- 🖼️ Broadcast Understanding & Generation
+- **550 complete broadcast games**
+- **12 single-camera games**
+- **300K+ temporal annotations**
+- **1M+ spatial annotations**
 
-Our goal is to provide the research community with **large-scale data, standardized evaluation protocols, and open benchmarks** for understanding the game of soccer.
+Beyond the datasets, SoccerNet organizes yearly international challenges where researchers and practitioners benchmark their methods on common tasks and evaluation protocols.
+
+🌐 **Website:** [soccer-net.org](https://www.soccer-net.org)
+
+---
 
-## 🤗 SoccerNet on Hugging Face
+## 🤗 Datasets on Hugging Face
 
-This organization hosts the official SoccerNet datasets, including challenge datasets and the original SoccerNet video data.
+The official SoccerNet Hugging Face organization currently hosts the following datasets.
 
-### Featured datasets
+### Core and research datasets
 
 | Dataset | Description |
 |---|---|
-| [SoccerNet_raw_HQ](https://huggingface.co/datasets/SoccerNet/SoccerNet_raw_HQ) | Original SoccerNet games in high-quality video |
-| [ActionAnticipation](https://huggingface.co/datasets/SoccerNet/ActionAnticipation) | Soccer action anticipation |
-| [SN-PCBAS-2026](https://huggingface.co/datasets/SoccerNet/SN-PCBAS-2026) | SoccerNet 2026 ball action spotting |
-| [SN-NVS-2026](https://huggingface.co/datasets/SoccerNet/SN-NVS-2026) | SoccerNet 2026 novel view synthesis |
-| [SN-VQA-2026](https://huggingface.co/datasets/SoccerNet/SN-VQA-2026) | SoccerNet 2026 visual question answering |
-| [SN-GSR-2025](https://huggingface.co/datasets/SoccerNet/SN-GSR-2025) | Game state reconstruction |
-| [SN-MVFouls-2025](https://huggingface.co/datasets/SoccerNet/SN-MVFouls-2025) | Multi-view foul recognition |
-| [SN-Depth-2025](https://huggingface.co/datasets/SoccerNet/SN-Depth-2025) | Soccer monocular depth estimation |
+| **[SoccerNet_raw_HQ](https://huggingface.co/datasets/SoccerNet/SoccerNet_raw_HQ)** | Gated repository for the SoccerNet raw high-quality data |
+| **[ActionAnticipation](https://huggingface.co/datasets/SoccerNet/ActionAnticipation)** | Action anticipation dataset used for the SoccerNet 2026 Action Anticipation Challenge |
+| **[SN-echoes](https://huggingface.co/datasets/SoccerNet/SN-echoes)** | SoccerNet-Echoes audio commentary dataset |
+| **[BannerReplacement](https://huggingface.co/datasets/SoccerNet/BannerReplacement)** | SoccerNet banner replacement dataset |
+
+### SoccerNet 2026
+
+| Dataset | Task |
+|---|---|
+| **[SN-PCBAS-2026](https://huggingface.co/datasets/SoccerNet/SN-PCBAS-2026)** | Player-Centric Ball Action Spotting |
+| **[SN-NVS-2026](https://huggingface.co/datasets/SoccerNet/SN-NVS-2026)** | Novel View Synthesis |
+| **[SN-VQA-2026](https://huggingface.co/datasets/SoccerNet/SN-VQA-2026)** | Visual Question Answering |
+
+### SoccerNet 2025
+
+| Dataset | Task |
+|---|---|
+| **[SN-BAS-2025](https://huggingface.co/datasets/SoccerNet/SN-BAS-2025)** | Ball Action Spotting |
+| **[SN-Depth-2025](https://huggingface.co/datasets/SoccerNet/SN-Depth-2025)** | Monocular Depth Estimation |
+| **[SN-MVFouls-2025](https://huggingface.co/datasets/SoccerNet/SN-MVFouls-2025)** | Multi-View Foul Recognition |
+| **[SN-GSR-2025](https://huggingface.co/datasets/SoccerNet/SN-GSR-2025)** | Game State Reconstruction |
+
+### SoccerNet 2024
+
+| Dataset | Task |
+|---|---|
+| **[SN-BAS-2024](https://huggingface.co/datasets/SoccerNet/SN-BAS-2024)** | Ball Action Spotting |
+| **[SN-MVFouls-2024](https://huggingface.co/datasets/SoccerNet/SN-MVFouls-2024)** | Multi-View Foul Recognition |
+| **[SN-GSR-2024](https://huggingface.co/datasets/SoccerNet/SN-GSR-2024)** | Game State Reconstruction |
 
 ➡️ **[Browse all SoccerNet datasets](https://huggingface.co/SoccerNet/datasets)**
 
+---
+
+## 🎯 Benchmark Tasks
+
+SoccerNet covers tasks ranging from low-level field and player localization to high-level understanding of complete soccer games.
+
+### 📺 Broadcast Video Understanding
+
+- **Action Spotting** — localize actions in time
+- **Replay Grounding** — retrieve a replayed action in a broadcast
+- **Camera Shot Segmentation** — segment camera shots and localize their boundaries
+- **Ball Action Spotting** — localize ball actions in time
+- **Dense Video Captioning** — spot salient moments and generate commentaries
+- **Multi-View Foul Recognition** — recognize fouls and their severity
+
+### 🏟️ Field Understanding
+
+- **Field Localization** — localize soccer pitch markings and goal posts
+- **Camera Calibration** — estimate camera calibration
+- **Monocular Depth Estimation** — estimate depth from a monocular camera
+
+### 👤 Player Understanding
+
+- **Player Tracking** — track multiple players through video sequences
+- **Player Re-Identification** — re-identify players across views
+- **Jersey Number Recognition** — identify player jersey numbers from tracklets
+
+### 🧠 Game Understanding
+
+- **Game State Reconstruction** — reconstruct the game state by tracking the ball and players in a top-view representation
+
+➡️ **[Explore all SoccerNet tasks](https://www.soccer-net.org/tasks)**
+
+---
+
 ## 🏆 SoccerNet Challenges
 
-SoccerNet organizes yearly international challenges bringing together researchers from academia and industry to benchmark state-of-the-art methods for soccer understanding.
+The **SoccerNet Challenges** have been organized yearly since 2021 and provide standardized datasets, development kits, evaluation protocols, and leaderboards for soccer understanding research.
+
+The **2026 edition**, the sixth SoccerNet Challenges edition, included:
+
+- **Spiideo SoccerNet SynLoc**
+- **Ball Action Anticipation**
+- **Visual Question Answering**
+- **Player-Centric Ball Action Spotting**
+- **Novel View Synthesis**
+- **FIFA Innovation Challenge — Skeletal Tracking Light** *(guest task)*
+
+Challenge resources are available through the SoccerNet website, Hugging Face datasets, GitHub development kits, and Codabench evaluation servers.
 
-Challenge datasets, development sets, test sets, and evaluation resources are progressively hosted here on Hugging Face.
+➡️ **[Explore the SoccerNet Challenges](https://www.soccer-net.org/challenges)**
 
-➡️ **[SoccerNet Challenges](https://www.soccer-net.org)**
+---
+
+## 💻 Open-Source Code
+
+Benchmark implementations, development kits, evaluation code, and research projects are maintained in the official **SoccerNet GitHub organization**.
+
+Some of the main repositories include:
+
+- [**sn-gamestate**](https://github.com/SoccerNet/sn-gamestate) — Game State Reconstruction
+- [**sn-calibration**](https://github.com/SoccerNet/sn-calibration) — Camera Calibration
+- [**sn-tracking**](https://github.com/SoccerNet/sn-tracking) — Player Tracking
+- [**sn-spotting**](https://github.com/SoccerNet/sn-spotting) — Action Spotting
+- [**sn-reid**](https://github.com/SoccerNet/sn-reid) — Player Re-Identification
+- [**sn-mvfoul**](https://github.com/SoccerNet/sn-mvfoul) — Multi-View Foul Recognition
+- [**sn-depth**](https://github.com/SoccerNet/sn-depth) — Monocular Depth Estimation
+- [**sn-echoes**](https://github.com/SoccerNet/sn-echoes) — SoccerNet-Echoes
+- [**sn-jersey**](https://github.com/SoccerNet/sn-jersey) — Jersey Number Recognition
+- [**sn-nvs**](https://github.com/SoccerNet/sn-nvs) — Novel View Synthesis
+
+➡️ **[Browse the SoccerNet GitHub organization](https://github.com/SoccerNet)**
+
+---
 
 ## 📚 Research
 
-SoccerNet was introduced as a benchmark for action spotting and has progressively expanded toward holistic understanding of soccer games, including temporal, spatial, player-centric, multi-view, and multimodal reasoning.
+SoccerNet research spans soccer video understanding, player and field analysis, multimodal understanding, and sports AI.
 
-Our datasets and benchmarks have supported research across **computer vision, video understanding, multimodal learning, sports analytics, and generative AI**.
+The project has grown from the original **SoccerNet action spotting benchmark released in 2018** to a suite of datasets, tasks, benchmark methods, yearly challenges, and community contributions.
 
-➡️ **[Publications](https://www.soccer-net.org/publications)**
+➡️ **[Browse SoccerNet publications](https://www.soccer-net.org/publications)**
 
-## 🤝 Join the Community
+---
 
-Whether you are working on a new benchmark, competing in the SoccerNet Challenges, or building the next generation of sports AI systems, we welcome contributions from the community.
+## 🤝 Join the SoccerNet Community
 
-**Website:** https://www.soccer-net.org  
-**GitHub:** https://github.com/SoccerNet  
-**Hugging Face:** https://huggingface.co/SoccerNet
+SoccerNet is an open research community bringing together researchers and practitioners working on **computer vision, video understanding, multimodal AI, sports analytics, and soccer**.
 
 <div align="center">
 
-### ⚽ Understand the Game.
-
-**Data • Benchmarks • Challenges • Community**
+[🌐 Website](https://www.soccer-net.org) ·
+[🤗 Hugging Face](https://huggingface.co/SoccerNet) ·
+[💻 GitHub](https://github.com/SoccerNet) ·
+[💬 Discord](https://discord.gg/cPbqf2mAwF)
 
 </div>