Added preprocessing steps

implementations/multimodal_representation_learning/README.md

@@ -17,7 +17,17 @@ The framework combines a Transformer-based tabular encoder with a ResNet-based i
 
 The repository includes two primary Jupyter notebooks demonstrating the capabilities and training processes of the TIP model:
 
-1. **`notebook_1_TIP.ipynb`**: **Training and Fine-Tuning**
+1. **`data_preprocessing.ipynb`**: **DVM-CAR Dataset Preparation**
+
+   * Guides users through downloading, cleaning, and preprocessing the DVM-CAR dataset.
+   * **Key Steps**:
+
+     * Downloading raw tabular data and images.
+     * Cleaning and processing tabular data using provided scripts.
+     * Converting images to numpy arrays for efficient training.
+     * Creating missing masks for incomplete-data fine-tuning experiments.
+
+2. **`notebook_1_TIP.ipynb`**: **Training and Fine-Tuning**
 
    * Provides an introduction to TIP and walks through the training process.
    * **Key Sections**:
@@ -28,7 +38,7 @@ The repository includes two primary Jupyter notebooks demonstrating the capabili
      * **Dataset**: Overview of the multimodal datasets used for training (images + tabular data).
      * **Fine-Tuning**: Fine-tuning the model in image-only, tabular-only, and multimodal modes.
 
-2. **`notebook_2_TIP.ipynb`**: **Evaluation, Comparison, and Metrics**
+3. **`notebook_2_TIP.ipynb`**: **Evaluation, Comparison, and Metrics**
 
    * Focuses on evaluating and comparing the performance of the model after fine-tuning.
    * **Key Sections**:
@@ -120,37 +130,37 @@ mkdir -p data/DVM/features
 
 #### 2. Clean and Process Tabular Data
 
-Use the provided notebook from the TIP repo:
+Run the **`data_preprocessing.ipynb`** notebook included in this repository:
 
-📄 [create\_dvm\_dataset.ipynb](https://github.com/siyi-wind/TIP/blob/main/data/create_dvm_dataset.ipynb)
-
-* Open the notebook.
+* Open `data_preprocessing.ipynb`.
 * Update paths to match your local `data/DVM/` setup.
-* Run all cells to generate cleaned and processed tabular files.
+* Run the tabular data cleaning section to generate cleaned and processed tabular files.
 
 ---
 
 #### 3. Convert Images to Numpy Arrays
 
-Use the helper script from the TIP repo:
-
-📄 [image2numpy.py](https://github.com/siyi-wind/TIP/blob/main/data/image2numpy.py)
+Continue with the **`data_preprocessing.ipynb`** notebook:
 
-* Update paths inside the script.
-* Run to convert JPG images into numpy format for faster training.
+* Run the image conversion section to convert JPG images into numpy format for faster training.
+* Update paths as needed within the notebook cells.
 
 ---
 
 #### 4. Create Missing Masks
 
-For incomplete-data fine-tuning experiments, generate missing masks:
+Complete the missing mask generation in **`data_preprocessing.ipynb`**:
 
-📄 [create\_missing\_mask.ipynb](https://github.com/siyi-wind/TIP/blob/main/data/create_missing_mask.ipynb)
-
-* Update paths to match `data/DVM/`.
-* Run all cells to create missing masks (RVM, RFM, MIFM, LIFM).
+* Run the missing mask creation section for incomplete-data fine-tuning experiments.
+* This generates missing masks (RVM, RFM, MIFM, LIFM).
 * Outputs will be stored in `data/DVM/missing_mask/`.
 
+Add the data_base path to **`configs/config_dvm_TIP.yaml`** based on your local setup:
+
+```yaml
+data_base: # TODO: set data base path 
+```
+
 ---
 
 ### Notes

implementations/multimodal_representation_learning/configs/config_dvm_TIP.yaml

@@ -25,7 +25,7 @@ lr_finder_lrs:
 multitarget:
 
 wandb_entity: # Removed this line
-data_base: /projects/aieng/multimodal_bootcamp/representation_learning/TIP/data/DVM/features
+data_base: # TODO: set data base path
 num_workers: 10
 pin_memory: True      # speeds up host→GPU transfer
 persistent_workers: True  # keeps workers alive between epochs