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 <div align="center">
-<img src="source/image/logo.jpg" alt="logo" width="500">
+<img src="source/_static/logo.jpg" alt="logo" width="500">
 </div>
 
 [![pypi](https://img.shields.io/pypi/v/GENetLib?logo=Pypi)](https://pypi.org/project/GENetLib)
@@ -23,7 +23,7 @@ This package also supports diverse output requirements:
 By integrating minimax concave penalty (MCP) and $L_2$-norm regularization within a neural network estimation framework, ``GENetLib`` offers an innovative solution for high-dimensional genetic data analysis. The framework is shown below.
 
 <div align="center">
-<img src="source/image/framework.png" alt="framework" width="600">
+<img src="source/_static/framework.png" alt="framework" width="600">
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 We provide a web-based documentation which introduces the meaning of function parameters, the usage of functions, detailed information about methods, and gives examples for each. The web page is available at