Update README.md

README.md

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 ## Overview
 
-The Earthwork Network Architecture (ENA) is the deep learning model designed to compare the accurate estimation prediction of earthwork quantities. This repository includes four distinct deep learning models—MLP, LSTM, Transformers, and LLM-based architectures (BERT)—tailored for automating and enhancing earthwork quantity estimation from CAD-based cross-sectional drawings.
+The Earthwork Network Architecture (ENA) is the deep learning model designed to compare the accurate estimation prediction of earthwork quantities in construction. This repository includes four distinct deep learning models—MLP, LSTM, Transformers, and LLM-based architectures (BERT)—tailored for automating and enhancing earthwork quantity estimation from CAD-based cross-sectional drawings.
+<img src="https://huggingface.co/mac999/earthwork-net-model/blob/main/doc/img3.webp" width="600"></br>
+<img src="https://huggingface.co/mac999/earthwork-net-model/blob/main/doc/img5.png" width="600">
 
 ### Key Features:
 1. **Multi-Model Approach**:
@@ -43,7 +45,7 @@ The Earthwork Network Architecture (ENA) is the deep learning model designed to
    - Employs a Half-Edge Topology Structure to tokenize and preprocess geometrical features.
 
 3. **Enhanced Performance**:
-   - Provides superior accuracy in Quantity Takeoff Classification (QTC) with reduced loss metrics.
+   - Provides superior accuracy in Quantity Takeoff Classification (QTC) for earthwork with reduced loss metrics.
    - Demonstrates robust generalization for unseen datasets, validated through a real-world road construction project.
 
 ### Research Basis: