From 363ed01d1a9c72514caceb32dda118d29c02d05c Mon Sep 17 00:00:00 2001
From: gongjy <2474590974@qq.com>
Date: Sat, 5 Oct 2024 09:48:58 +0800
Subject: [PATCH] update readme

---
 README.md    | 2 +-
 README_en.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index cf26082..5f23162 100644
--- a/README.md
+++ b/README.md
@@ -511,7 +511,7 @@ python web_server.py
 - 值得改进的方面：
   - 更简单的Projection的跨模态特征对齐方式，相较于Cross-Attention会带来更大的性能损失。
   - Clip模型可以尝试更大性能更强的large系列，用更具细粒度的token表征图像特征，目前仍非常粗糙。
-  - 分辨率不高，理论上只有224*224（minimind-v数据集为节省空间，仅设定为128*128）。
+  - 分辨率不高，理论上只有224×224（minimind-v数据集为节省空间，仅设定为128×128）。
   - ...
 
 ---
diff --git a/README_en.md b/README_en.md
index d255daf..1480a92 100644
--- a/README_en.md
+++ b/README_en.md
@@ -497,7 +497,7 @@ Based on the provided table data, the performance of the four models can be summ
 - Areas for improvement:
   - Simpler projection methods for cross-modal feature alignment result in greater performance loss compared to Cross-Attention.
   - Larger and more powerful large-series Clip models can be tried, using more fine-grained token representations for image features, which are currently very rough.
-  - The resolution is not high, theoretically only 224*224 (minimind-v dataset is set to 128*128 to save space).
+  - The resolution is not high, theoretically only 224×224 (minimind-v dataset is set to 128×128 to save space).
   - ...
 
 ---