Support Light-HQ-SAM

README.md

@@ -17,6 +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**
+- **`2023/07/25`** 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!
 - **`2023/07/14`** Combining **Grounding-DINO-B** with [SAM-HQ](https://github.com/SysCV/sam-hq) achieves **49.6 mean AP** in [Segmentation in the Wild](https://eval.ai/web/challenges/challenge-page/1931/overview) competition zero-shot track, surpassing Grounded-SAM by **3.6 mean AP**, thanks for their great work!
 - **`2023/06/28`** Combining Grounding-DINO with Efficient SAM variants including [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) and [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) in [EfficientSAM](./EfficientSAM/) for faster annotating, thanks a lot for their great work!
 - **`2023/06/20`** By combining **Grounding-DINO-L** with **SAM-ViT-H**, Grounded-SAM achieves 46.0 mean AP in [Segmentation in the Wild](https://eval.ai/web/challenges/challenge-page/1931/overview) competition zero-shot track on [CVPR 2023 workshop](https://computer-vision-in-the-wild.github.io/cvpr-2023/), surpassing [UNINEXT (CVPR 2023)](https://github.com/MasterBin-IIAU/UNINEXT) by about **4 mean AP**.