Update README.md

README.md

@@ -2,61 +2,88 @@
 license: mit
 pipeline_tag: object-detection
 tags:
-- climate
+- tensorflow
+- keras
+- opencv
+- wall-crack-detection
+- real-time
+- mobile
 datasets: []
+---
+
+# Wall Crack Detection Model
+
+This project uses a **deep learning model** to detect cracks in walls using **real-time video feed** from a mobile phone camera.  
+It is built with **TensorFlow**, **Keras**, and **OpenCV**.
 
 ---
-# Wall Crack Detection
 
-This project uses a deep learning model to detect cracks in walls using real-time video feed from a mobile phone camera. 
-The project is built using Tensorflow, Keras, and OpenCV.
+## Model Overview
 
+- **Model Type:** Object Detection (Binary – Crack / No Crack)  
+- **Framework:** TensorFlow / Keras  
+- **File:** `crack_detector.h5`  
+- **Input:** Image frame (from video feed or camera)  
+- **Output:** Crack detection result (with bounding boxes or classification)
+
+---
 
 ## Project Structure
-```
-|-- crack_detector.h5 # Trained Model
-|--main.py # Real-time detection script
-|--concrete_data/ #Dataset folder (if training from scratch is required)
-||--train/
-|||-Positive/
-|||-Negative/
-||--val/
-|||-Positive/
-|||-Negative/
-|--detection_log.csv # Optional, it will help you to store predictions
-|--README.md 
-```
-Requirements
 
-```
-- python 3.10+
-- Tensorflow 2.x
+```bash
+project/
+│-- crack_detector.h5       # Trained model file
+│-- main.py                 # Real-time detection script
+│-- concrete_data/          # Dataset folder (if training from scratch)
+│   ├── train/
+│   │   ├── Positive/
+│   │   └── Negative/
+│   └── val/
+│       ├── Positive/
+│       └── Negative/
+│-- detection_log.csv        # Optional log for predictions
+│-- README.md
+
+
+
+---
+
+## Requirements
+
+- Python 3.10+
+- TensorFlow 2.x
 - OpenCV
-- Numpy
+- NumPy
+
+### Install Dependencies
+```bash
+pip install tensorflow opencv-python numpy
 ```
-Install Dependencies using pip:
+For Best Results, create a virtual Environment:
 ```
+Using Conda:
+conda --version
+conda create --name wallcrack-env python=3.10
+conda activate wallcrack-env
 pip install tensorflow opencv-python numpy
 ```
-For better experience make a virtual environment.
 
-1. conda create --name <environment-name> python=3.10
 
-2. conda activate <environment-name> 
-conda deactivate
+### Usage
 
-conda activatae <environment-name>
+Set up DroidCam IP or any camera stream URL in main.py
 
+Run the detection script:
+```
+python main.py
+```
+The model will process live video feed and detect cracks in walls in real time.
 
-pip install tensorflow opencv-python numpy
 
 
-conda --version
+###  Notes
+
+If you want to retrain, use images in the concrete_data folder.
+
+The detection_log.csv file can store timestamped predictions for later analysis.
 
-anaconda install 
-1. conda --version
-2. conda create --name <environment-name> python=3.10
-3. conda activate <environment-name>
-4. pip install tensorflow opencv-python numpy
-5. DroidCam IP in main.py file <url>
-6. run python main.py