From d1c510118c0fdffb87bab3cd3dfe3e6b02a36f8b Mon Sep 17 00:00:00 2001
From: Ren Tianhe <48727989+rentainhe@users.noreply.github.com>
Date: Mon, 11 Dec 2023 14:29:12 +0800
Subject: [PATCH] Refine README efficient-sam demo link

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 913380d7..78e0952e 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ We are very willing to **help everyone share and promote new projects** based on
 The **core idea** behind this project is to **combine the strengths of different models in order to build a very powerful pipeline for solving complex problems**. And it's worth mentioning that this is a workflow for combining strong expert models, where **all parts can be used separately or in combination, and can be replaced with any similar but different models (like replacing Grounding DINO with GLIP or other detectors / replacing Stable-Diffusion with ControlNet or GLIGEN/ Combining with ChatGPT)**.
 
 **🍇 Updates**
-- **`2023/12/10`** Support [Grounded-Efficient-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/add_efficient_sam/EfficientSAM#run-grounded-efficient-sam-demo) demo, thanks a lot for their great work!
+- **`2023/12/10`** Support [Grounded-Efficient-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-efficient-sam-demo) demo, thanks a lot for their great work!
 - **`2023/11/24`** Release [RAM++](https://arxiv.org/abs/2310.15200), which is the next generation of RAM. RAM++ can recognize any category with high accuracy, including both predefined common categories and diverse open-set categories.
 - **`2023/11/23`** Release our newly proposed visual prompt counting model [T-Rex](https://github.com/IDEA-Research/T-Rex). The introduction [Video](https://www.youtube.com/watch?v=engIEhZogAQ) and [Demo](https://deepdataspace.com/playground/ivp) is available in [DDS](https://github.com/IDEA-Research/deepdataspace) now.
 - **`2023/07/25`** Support [Light-HQ-SAM](https://github.com/SysCV/sam-hq) in [EfficientSAM](./EfficientSAM/), credits to [Mingqiao Ye](https://github.com/ymq2017) and [Lei Ke](https://github.com/lkeab), thanks a lot for their great work!