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+# Quick Start Guide - Testing the Model
+
+This guide will help you quickly test the Highway Vehicle Detection model on new data.
+
+## Prerequisites
+
+1. **Install Python dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. **Download/clone the repository** from Hugging Face:
+   ```bash
+   git clone https://huggingface.co/bichuche0705/highway-vehicle-detection-code
+   cd highway-vehicle-detection-code
+   ```
+
+   Or download manually from: https://huggingface.co/bichuche0705/highway-vehicle-detection-code
+
+## Model Files Location
+
+The trained models are located at:
+- **Best model (recommended)**: `training_runs/yolov8m_stage2_improved/weights/best.pt`
+- **Alternative location**: `models/yolov8m_stage2_improved_best.pt`
+- **Stage 1 model**: `training_runs/yolov8m_stage1_smart/weights/best.pt`
+
+## Method 1: Simple Test Script (Recommended for Quick Testing)
+
+Use the `test_model.py` script for easy testing:
+
+### Test on an Image:
+```bash
+python test_model.py your_image.jpg
+```
+
+The script will:
+- Automatically find the model
+- Detect vehicles in the image
+- Save the result as `your_image_result.jpg`
+
+### Test on a Video:
+```bash
+python test_model.py your_video.mp4 --video
+```
+
+With custom output:
+```bash
+python test_model.py your_video.mp4 --video --output output_video.mp4
+```
+
+## Method 2: Using example_usage.py
+
+```python
+from example_usage import detect_vehicles, process_video
+
+# For images
+detect_vehicles("test_image.jpg", model_path="models/yolov8m_stage2_improved_best.pt")
+
+# For videos
+process_video("test_video.mp4", output_path="output.mp4")
+```
+
+## Method 3: Using main.py (Full Application)
+
+The `main.py` script provides advanced features like vehicle counting and tracking:
+
+```bash
+# Basic usage (auto-detects first .mp4 in directory)
+python main.py --model training_runs/yolov8m_stage2_improved/weights/best.pt
+
+# With specific video
+python main.py --model training_runs/yolov8m_stage2_improved/weights/best.pt --video your_video.mp4
+
+# Save output video
+python main.py --model training_runs/yolov8m_stage2_improved/weights/best.pt --video input.mp4 --output output.mp4
+```
+
+**Important**: You need to specify the `--model` path because the default path in `main.py` might not match the repository structure.
+
+## Method 4: Direct Python Code
+
+```python
+from ultralytics import YOLO
+
+# Load model
+model = YOLO('training_runs/yolov8m_stage2_improved/weights/best.pt')
+
+# Test on image
+results = model('your_image.jpg')
+results[0].show()  # Display results
+results[0].save('output.jpg')  # Save results
+
+# Test on video
+results = model('your_video.mp4', save=True)
+# Output saved to: runs/detect/predict/
+```
+
+## Detected Vehicle Classes
+
+The model detects 8 vehicle types:
+1. **auto** - Three-wheelers
+2. **bus** - Public transport vehicles
+3. **car** - Passenger cars
+4. **lcv** - Light Commercial Vehicles
+5. **motorcycle** - Two-wheelers
+6. **multiaxle** - Multi-axle heavy vehicles
+7. **tractor** - Agricultural/construction vehicles
+8. **truck** - Heavy vehicles
+
+## Troubleshooting
+
+### Model Not Found Error
+
+If you get "Model file not found":
+1. Make sure you've downloaded/cloned the repository completely
+2. Check that the model file exists at one of these paths:
+   - `training_runs/yolov8m_stage2_improved/weights/best.pt`
+   - `models/yolov8m_stage2_improved_best.pt`
+
+### Import Errors
+
+If you get import errors:
+```bash
+pip install ultralytics opencv-python numpy torch torchvision
+```
+
+Or install all requirements:
+```bash
+pip install -r requirements.txt
+```
+
+### CUDA/GPU Issues
+
+If you have CUDA available, PyTorch will use it automatically. For CPU-only:
+- The model will run but will be slower
+- No additional setup needed
+
+## Example Output
+
+When running the model, you should see:
+- Bounding boxes around detected vehicles
+- Class labels (auto, bus, car, etc.)
+- Confidence scores
+- Output saved to files
+
+## Need Help?
+
+Check the repository README.md for more detailed documentation, or review the code comments in:
+- `main.py` - Full application with counting
+- `example_usage.py` - Simple examples
+- `test_model.py` - Quick test script
+