Update README.md

README.md

@@ -52,9 +52,13 @@ The ENA is detailed in the paper *Earthwork Network Architecture (ENA): Research
 ## Usage
 
 ### Prerequisites
-- **Programming Language**: Python 3.8 or above.
+- **Programming Language**: Python 3.8 or above. PyTorch (torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118)
 - **Libraries**: Install the required libraries using `pip install`. Detailed dependencies will be provided in the code files.
-
+   ```bash
+   pip install json os re logging torch numpy matplotlib seaborn transformers scikit-learn tqdm
+   pip install pandas scipy trimesh laspy open3d pyautocad pywin32
+   ```
+   
 ### 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. 
@@ -67,7 +71,7 @@ The ENA is detailed in the paper *Earthwork Network Architecture (ENA): Research
    python prepare_dataset.py --input output/ --output dataset/
    ```
 
-2. **Training Data**:
+2. **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]
@@ -76,7 +80,7 @@ The ENA is detailed in the paper *Earthwork Network Architecture (ENA): Research
 3. **Run and Test 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 --model_type [MLP|LSTM|Transformer|LLM]
+   python ena_run_model.py
    ```     
    
 ### Training and Evaluation