Update README.md

README.md

@@ -62,40 +62,30 @@ The ENA is detailed in the paper *Earthwork Network Architecture (ENA): Research
    ```
    
 ### Data Preparation
-1. **Prepare Train Dataset**:
-   - Prepare CAD cross-sectional drawings as input files and load it on Autocad. Run the below program to extract the entities per each cross-section in the drawing. In addition, you can define the earthwork item's layer name in config.json. 
+1. **Prepare Drawing File**:
+   - Prepare CAD cross-sectional drawings as input files and load it on Autocad (./input/cross_section_sample_drawing.dwg). Run the below program to extract the entities per each cross-section in the drawing. In addition, you can define the earthwork item's layer name in config.json. 
    ```bash
    python create_earthwork_dataset.py --config config.json --output output/ --view output/chain_chunk_6.json
    ```
-   - In reference, we assume that each earthwork item's layer including entities were segmented(Please refer to the below paper). 
+   - In Autocad, you can select the cross-section entities using command powered by create_earthwork_dataset program. In reference, we assume that each earthwork item's layer including entities were segmented(Please refer to the below paper).
+
+2. **Prepare Train Dataset**:
    - Use the provided scripts to preprocess and tokenize geometrical features.
    ```bash
    python prepare_dataset.py --input output/ --output dataset/
    ```
 
-2. **Training Data (TBD)**:
+### Training and Evaluation
+1. **Training Data (TBD)**:
    - Features are tokenized into sequences for MLP, LSTM, Transformers, and LLM models. We'll upload the train source file after arrangement.
    ```bash
    python train_ena_model.py --model_type [MLP|LSTM|Transformer|LLM]
    ```
-
-3. **Run and Test ENA model**:
+2. **Run and Evaluate ENA model**:
    - Run the below program to run and test the each ENA model. It will generate log and graph image files to check the performance.
    ```bash
    python ena_run_model.py
    ```     
-   
-### Training and Evaluation
-1. Select the model architecture (`MLP`, `LSTM`, `Transformer`, or `LLM`).
-2. Configure hyperparameters (batch size, learning rate, etc.) as required.
-3. Run the training script:
-   ```bash
-   python train_ena_model.py --model_type [MLP|LSTM|Transformer|LLM]
-   ```
-4. Evaluate the model using the test dataset:
-   ```bash
-   python evaluate_ena_model.py --model_path [path/to/trained/model]
-   ```
 
 ## Results
 - **Best Model**: LLM-based ENA achieved a QTC accuracy of **97.17%**, outperforming other architectures in accuracy and stability.