Update README.md

README.md

@@ -83,7 +83,7 @@ The goal is to measure whether models can reliably perceive the critical **Visua
 
 ## Download
 
-EPIC-Bench contains ~35,000 small files across three task categories. To work around HuggingFace's per-file rate limit and to keep download speed reasonable, the annotations are distributed as **three task-level tarballs** instead of raw folders.
+EPIC-Bench contains ~35k small files across three task categories. To work around HuggingFace's per-file rate limit and to keep download speed reasonable, the annotations are distributed as **three task-level tarballs** instead of raw folders.
 
 Download the tarballs from [HuggingFace](https://huggingface.co/datasets/rxc205/EPIC-Bench) (or [ModelScope](https://www.modelscope.cn/datasets/macarich/EPIC-Bench)):
 
@@ -113,12 +113,6 @@ for f in Manipulation.tar.gz Navigation.tar.gz TargetLocalization.tar.gz; do
 done
 ```
 
-## ✨ Highlights
-
--  **Embodied-Scenario** evaluation of VLM **Visual Perception** capability.
--  Focus on **Visual Grounding / Perception** without language shortcut exploitation.
--  **Diverse** and **Fine-Grained** task design.
-
 ## 🧰 EPIC-Bench Evaluation Toolkit
 
 We provide [evaluation code](https://github.com/rxc205/EPIC-Bench-Eval#-epic-bench-evaluation-toolkit) for both open-source VLMs via **ms-swift** and API-based VLMs via **lmms-eval**.