Update README.md

README.md

@@ -18,12 +18,20 @@ arxiv: 2508.05630
 
 # MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes
 
+🔥 [Evaluation Server](https://www.codabench.org/competitions/10062/) | 🏠 [Homepage](https://mose.video) | 📄 [Paper](https://arxiv.org/pdf/2508.05630) | 🔗 [GitHub](https://github.com/henghuiding/MOSE-api)
+
+## Download
+We recommend using `huggingface-cli` to download:
+```
+pip install -U "huggingface_hub[cli]"
+huggingface-cli download FudanCVL/MOSEv2 --repo-type dataset --local-dir ./MOSEv2 --local-dir-use-symlinks False --max-workers 16
+```
+ 
+
 ## Dataset Summary
 
 MOSEv2 is a comprehensive video object segmentation dataset designed to advance VOS methods under real-world conditions. It consists of **5,024 videos** and over **701,976 high-quality masks** for **10,074 objects** across **200 categories**.
 
-🏠 [Homepage](https://mose.video) | 📄 [Paper](https://arxiv.org/pdf/2508.05630) | 🔗 [GitHub](https://github.com/henghuiding/MOSE-api)
-
 ## Dataset Description
 
 Video object segmentation (VOS) aims to segment specified target objects throughout a video. Although state-of-the-art methods have achieved impressive performance (e.g., 90+% J&F) on existing benchmarks such as DAVIS and YouTube-VOS, these datasets primarily contain salient, dominant, and isolated objects, limiting their generalization to real-world scenarios. To advance VOS toward more realistic environments, coMplex video Object SEgmentation (MOSEv1) was introduced to facilitate VOS research in complex scenes. Building on the strengths and limitations of MOSEv1, we present MOSEv2, a significantly more challenging dataset designed to further advance VOS methods under real-world conditions.