Upload README.md with huggingface_hub

README.md

@@ -49,7 +49,7 @@ tags:
 
 Large Language Models (LLMs) demonstrate strong capabilities in broad knowledge representation, yet they are inherently deficient in pixel-level perceptual understanding. Although the Segment Anything Model (SAM) represents a significant advancement in visual-prompt-driven image segmentation, it exhibits notable limitations in multi-mask prediction and category-specific segmentation tasks, and it cannot integrate all segmentation tasks within a unified model architecture. To address these limitations, we present X-SAM, a streamlined Multimodal Large Language Model (MLLM) framework that extends the segmentation paradigm from *segment anything* to *any segmentation*. Specifically, we introduce a novel unified framework that enables more advanced pixel-level perceptual comprehension for MLLMs. Furthermore, we propose a new segmentation task, termed Visual GrounDed (VGD) segmentation, which segments all instance objects with interactive visual prompts and empowers MLLMs with visual grounded, pixel-wise interpretative capabilities. To enable effective training on diverse data sources, we present a unified training strategy that supports co-training across multiple datasets. Experimental results demonstrate that X-SAM achieves state-of-the-art performance on a wide range of image segmentation benchmarks, highlighting its efficiency for multimodal, pixel-level visual understanding.
 
-**More details can be found in [Github Page](https://wanghao9610.github.io/X-SAM/).**
+👉 **More details can be found in [GitHub](https://github.com/wanghao9610/X-SAM).**
 
 ## 📌 Citation
 If you find X-SAM is helpful for your research or applications, please consider giving us a like 💖 and citing it by the following BibTex entry.