Update README.md

README.md

@@ -20,7 +20,21 @@ tags:
 
 ## Overview
 This project demonstrates a depth estimation model that predicts the average depth of images using features extracted from a pre-trained ResNet50 model and an XGBoost regressor. The model was trained using the **NYUv2 dataset** hosted on Hugging Face ([0jl/NYUv2](https://huggingface.co/datasets/0jl/NYUv2)). The trained model is saved as `model.pkl` using Python's `pickle` library for easy deployment and reuse.
+### Loading the Model
+The model is saved as `model.pkl` using `pickle`. You can load and use it as follows:
+
+```python
+import pickle
 
+# Load the trained model
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Example usage
+features = extract_features("path/to/image.jpg")  # Use the same feature extraction pipeline
+predicted_depth = model.predict([features])
+print("Predicted Depth:", predicted_depth[0])
+```
 ## Features
 - **Model Architecture**:
   - Feature extraction: ResNet50 (pre-trained on ImageNet, with the top layers removed and global average pooling).
@@ -41,47 +55,33 @@ This project demonstrates a depth estimation model that predicts the average dep
    - Preprocessing: Images were normalized using the `preprocess_input` function from TensorFlow's ResNet50 module.
 2. **Regression**:
    - XGBoost regressor was trained on the extracted features to predict average depth values.
-   - Hyperparameters were tuned using cross-validation for optimal performance.
+   - Hyperparameters were tuned using cross-validation techniques for optimal performance.
 
 ## Results
-- **R² Score**: 0.741 (indicates the model explains 74.1% of the variance in depth prediction).
-- Performance is reasonable for a first implementation and can be further improved with additional tuning or feature extraction methods.
+- **R² Score**: 0.841.
+- Performance is reasonable for a first few implementation and can be further improved with additional tuning or by improving feature extraction methods.
 
 ## How to Use
 ### Requirements
 1. Python 3.8+
 2. Required libraries:
    - `numpy`
-   - `tensorflow`
-   - `xgboost`
    - `pickle`
-   - `opencv-python`
+   - `xgboost`
    - `datasets`
+   - `tensorflow`
+   - 'scikit-learn'
+   - `opencv-python`
+
 
 Install the dependencies using pip:
 ```bash
-pip install numpy tensorflow xgboost pickle-mixin opencv-python datasets
-```
-
-### Loading the Model
-The model is saved as `model.pkl` using `pickle`. You can load and use it as follows:
-
-```python
-import pickle
-
-# Load the trained model
-with open("model.pkl", "rb") as f:
-    model = pickle.load(f)
-
-# Example usage
-features = extract_features("path/to/image.jpg")  # Use the same feature extraction pipeline
-predicted_depth = model.predict([features])
-print("Predicted Depth:", predicted_depth[0])
+pip install numpy tensorflow xgboost pickle-mixin opencv-python datasets scikit-learn
 ```
 
 ### Training Pipeline
 If you want to retrain the model, follow these steps:
-NOTE: This pipline has just the overlook and the basic parameters more additional parameter tunings and preprocessing steps were being conducted during the training of the original model 
+NOTE: This pipeline has just the base fundamental code more additional parameter tunings and preprocessing steps were being conducted during the training of the original model 
 1. Download the **NYUv2 dataset** from Hugging Face:
    ```python
    from datasets import load_dataset
@@ -89,38 +89,27 @@ NOTE: This pipline has just the overlook and the basic parameters more additiona
    ```
 2. Extract features using ResNet50:
    ```python
-   from tensorflow.keras.applications import ResNet50
-   from tensorflow.keras.applications.resnet50 import preprocess_input
-   import numpy as np
 
-   # Load ResNet50 model
    model = ResNet50(weights="imagenet", include_top=False, pooling="avg")
 
    from PIL import Image
    def extract_features(image_path):
-       image = image.resize((224, 224))
-       image_array = np.array(image)
-       image_array = np.expand_dims(image_array, axis=0).astype("float32")
        image_array = preprocess_input(image_array)
-       
        features = model.predict(image_array)
        return features.flatten()
    ```
 3. Train the XGBoost regressor on the extracted features and save the model:
    ```python
-   from xgboost import XGBRegressor
-   import pickle
 
    regressor = XGBRegressor()
    regressor.fit(X_train, y_train)
 
-   # Save the trained model
    with open("model.pkl", "wb") as f:
        pickle.dump(regressor, f)
    ```
 
 ## License
-This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for more information.
+This project is licensed under the Apache License 2.0.
 
 ## Author
 **Vishal Adithya.A**