Update README.md

README.md

@@ -1,208 +1,208 @@
----
-license: mit
-tags:
-- computer-vision
-- object-detection
-- yolov8
-- vehicle-detection
-- traffic-analysis
-- highway-monitoring
-library_name: ultralytics
-pipeline_tag: object-detection
----
-
-# Highway Vehicle Detection - Code & Models
-
-A complete vehicle detection system for highway traffic monitoring. This repository contains the trained models, source code, and documentation - ready to use without requiring dataset downloads.
-
-## Quick Start
-
-### Installation
-```bash
-pip install ultralytics opencv-python numpy
-```
-
-### Basic Usage
-```python
-from ultralytics import YOLO
-
-# Load the trained model
-model = YOLO('models/yolov8m_stage2_improved_best.pt')
-
-# Run inference on an image
-results = model('path/to/image.jpg')
-results[0].show()
-
-# Process video
-results = model('path/to/video.mp4', save=True)
-```
-
-### Using the Main Application
-```bash
-python main.py
-```
-
-## Repository Contents
-
-### Trained Models
-- `models/yolov8m_stage2_improved_best.pt` - **Final model** (recommended)
-- `models/yolov8m_stage1_smart_best.pt` - Stage 1 model (for comparison)
-
-### Source Code
-- `main.py` - Complete vehicle detection and counting application
-- `example_usage.py` - Simple usage examples
-- `requirements.txt` - Python dependencies
-- `test_improved_model.bat` - Windows testing script
-
-### Fine-tuning Dataset
-- `finetune_dataset/images/` - 92 fine-tuning images
-- `finetune_dataset/labels/` - Corresponding annotation files
-- `finetune_dataset/README.dataset.txt` - Dataset information
-- `finetune_dataset/README.roboflow.txt` - Roboflow export info
-
-### Configuration
-- `dataset_configs/main_data.yaml` - Main dataset configuration (8 classes)
-- `dataset_configs/finetune_data.yaml` - Fine-tuning dataset configuration
-
-### Training Logs
-- `training_logs/stage2_results.png` - Training results visualization
-- `training_logs/stage2_confusion_matrix.png` - Confusion matrix
-- `training_logs/stage2_results.csv` - Detailed training metrics
-- `training_logs/stage2_val_batch0_pred.jpg` - Sample validation predictions
-
-### Training Runs Structure
-- `training_runs/stage1_smart/` - Stage 1 training configuration and weights
-  - `args.yaml` - Training arguments
-  - `weights/last.pt` - Last epoch weights
-- `training_runs/stage2_improved/` - Stage 2 training configuration and weights
-  - `args.yaml` - Training arguments
-  - `weights/last.pt` - Last epoch weights
-  - `BoxF1_curve.png` - F1 score curve
-  - `BoxPR_curve.png` - Precision-Recall curve
-  - `labels.jpg` - Label distribution visualization
-
-### Documentation
-- `PROJECT_REPORT.md` - Complete project documentation
-- `README.md` - This file
-
-## Model Performance
-
-### Classes Detected
-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
-
-### Training Stages
-- **Stage 1**: Initial training on 8,219 highway images
-- **Stage 2**: Fine-tuning on 92 additional images for improved truck/bus detection
-
-### Fine-tuning Dataset Details
-- **Images**: 92 carefully selected highway images
-- **Focus**: Improved detection of trucks and buses
-- **Classes**: Enhanced examples for problematic vehicle types
-- **Format**: YOLO format with bounding box annotations
-- **Quality**: High-quality images with clear vehicle visibility
-
-## External Resources
-
-### Test Video
-Watch the model in action on YouTube:
-[Highway Vehicle Detection Demo](https://www.youtube.com/watch?v=wqctLW0Hb_0&list=PLJKyZ_NuOhJQzif2-6-Kq9OiOj_UjJWvi)
-
-### Main Dataset
-Download the complete training dataset from Kaggle:
-[Vehicle Detection 8 Classes Dataset](https://www.kaggle.com/datasets/sakshamjn/vehicle-detection-8-classes-object-detection/data)
-
-## Technical Details
-
-- **Architecture**: YOLOv8m (Medium)
-- **Framework**: Ultralytics YOLO
-- **Input**: Images/Videos
-- **Output**: Bounding boxes with class labels and confidence scores
-- **Hardware**: CPU/GPU compatible
-
-## Usage Examples
-
-### Vehicle Detection
-```python
-from ultralytics import YOLO
-import cv2
-
-# Load the final model
-model = YOLO('models/yolov8m_stage2_improved_best.pt')
-
-# Detect vehicles in image
-results = model('highway_image.jpg')
-
-# Process results
-for result in results:
-    boxes = result.boxes
-    for box in boxes:
-        x1, y1, x2, y2 = box.xyxy[0]
-        conf = box.conf[0]
-        cls = int(box.cls[0])
-        class_name = model.names[cls]
-        print(f"Detected: {class_name} (confidence: {conf:.2f})")
-```
-
-### Video Processing with Counting
-```python
-# Process video with vehicle counting
-results = model('traffic_video.mp4', save=True, save_txt=True)
-
-# The main.py script provides advanced counting and tracking features
-```
-
-### Using the Complete Application
-```python
-# Run the full application with counting and visualization
-from main import VehicleCounter
-
-counter = VehicleCounter()
-counter.process_video('input_video.mp4', 'output_video.mp4')
-```
-
-## Applications
-
-- Highway traffic monitoring
-- Vehicle counting and classification
-- Traffic flow analysis
-- Automated surveillance systems
-- Road safety monitoring
-- Traffic data collection
-
-## Related Repositories
-
-- **Full Dataset**: [highway-vehicle-detection-full](https://huggingface.co/datasets/bichuche0705/highway-vehicle-detection-full) - Complete project with datasets and videos
-- **Model Only**: [highway-vehicle-detection](https://huggingface.co/bichuche0705/highway-vehicle-detection) - Just the trained model
-
-## License
-
-MIT License - Free to use for research and commercial purposes
-
-## Contributing
-
-This is a research project. For questions or improvements, please contact the author.
-
-## Contact
-
-**Author**: Nguyen Quoc Viet  
-**Repository**: https://huggingface.co/bichuche0705/highway-vehicle-detection-code
-
-## Citation
-
-If you use this model in your research, please cite:
-
-```bibtex
-@misc{highway-vehicle-detection-code,
-  title={Highway Vehicle Detection - Code \& Models},
-  author={Nguyen Quoc Viet},
-  year={2024},
-  url={https://huggingface.co/bichuche0705/highway-vehicle-detection-code}
-}
-```
+---
+license: mit
+tags:
+- computer-vision
+- object-detection
+- yolov8
+- vehicle-detection
+- traffic-analysis
+- highway-monitoring
+library_name: ultralytics
+pipeline_tag: object-detection
+---
+
+# Highway Vehicle Detection - Code & Models
+
+A complete vehicle detection system for highway traffic monitoring. This repository contains the trained models, source code, and documentation - ready to use without requiring dataset downloads.
+
+## Quick Start
+
+### Installation
+```bash
+pip install ultralytics opencv-python numpy
+```
+
+### Basic Usage
+```python
+from ultralytics import YOLO
+
+# Load the trained model
+model = YOLO('models/yolov8m_stage2_improved_best.pt')
+
+# Run inference on an image
+results = model('path/to/image.jpg')
+results[0].show()
+
+# Process video
+results = model('path/to/video.mp4', save=True)
+```
+
+### Using the Main Application
+```bash
+python main.py
+```
+
+## Repository Contents
+
+### Trained Models
+- `models/yolov8m_stage2_improved_best.pt` - **Final model** (recommended)
+- `models/yolov8m_stage1_smart_best.pt` - Stage 1 model (for comparison)
+
+### Source Code
+- `main.py` - Complete vehicle detection and counting application
+- `example_usage.py` - Simple usage examples
+- `requirements.txt` - Python dependencies
+- `test_improved_model.bat` - Windows testing script
+
+### Fine-tuning Dataset
+- `finetune_dataset/images/` - 92 fine-tuning images
+- `finetune_dataset/labels/` - Corresponding annotation files
+- `finetune_dataset/README.dataset.txt` - Dataset information
+- `finetune_dataset/README.roboflow.txt` - Roboflow export info
+
+### Configuration
+- `dataset_configs/main_data.yaml` - Main dataset configuration (8 classes)
+- `dataset_configs/finetune_data.yaml` - Fine-tuning dataset configuration
+
+### Training Logs
+- `training_logs/stage2_results.png` - Training results visualization
+- `training_logs/stage2_confusion_matrix.png` - Confusion matrix
+- `training_logs/stage2_results.csv` - Detailed training metrics
+- `training_logs/stage2_val_batch0_pred.jpg` - Sample validation predictions
+
+### Training Runs Structure
+- `training_runs/stage1_smart/` - Stage 1 training configuration and weights
+  - `args.yaml` - Training arguments
+  - `weights/last.pt` - Last epoch weights
+- `training_runs/stage2_improved/` - Stage 2 training configuration and weights
+  - `args.yaml` - Training arguments
+  - `weights/last.pt` - Last epoch weights
+  - `BoxF1_curve.png` - F1 score curve
+  - `BoxPR_curve.png` - Precision-Recall curve
+  - `labels.jpg` - Label distribution visualization
+
+### Documentation
+- `PROJECT_REPORT.md` - Complete project documentation
+- `README.md` - This file
+
+## Model Performance
+
+### Classes Detected
+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
+
+### Training Stages
+- **Stage 1**: Initial training on 8,219 highway images
+- **Stage 2**: Fine-tuning on 92 additional images for improved truck/bus detection
+
+### Fine-tuning Dataset Details
+- **Images**: 92 carefully selected highway images
+- **Focus**: Improved detection of trucks and buses
+- **Classes**: Enhanced examples for problematic vehicle types
+- **Format**: YOLO format with bounding box annotations
+- **Quality**: High-quality images with clear vehicle visibility
+
+## External Resources
+
+### Test Video
+Watch the model in action on YouTube:
+[Highway Vehicle Detection Demo](https://www.youtube.com/watch?v=wqctLW0Hb_0&list=PLJKyZ_NuOhJQzif2-6-Kq9OiOj_UjJWvi)
+
+### Main Dataset
+Download the complete training dataset from Kaggle:
+[Vehicle Detection 8 Classes Dataset](https://www.kaggle.com/datasets/sakshamjn/vehicle-detection-8-classes-object-detection/data)
+
+## Technical Details
+
+- **Architecture**: YOLOv8m (Medium)
+- **Framework**: Ultralytics YOLO
+- **Input**: Images/Videos
+- **Output**: Bounding boxes with class labels and confidence scores
+- **Hardware**: CPU/GPU compatible
+
+## Usage Examples
+
+### Vehicle Detection
+```python
+from ultralytics import YOLO
+import cv2
+
+# Load the final model
+model = YOLO('models/yolov8m_stage2_improved_best.pt')
+
+# Detect vehicles in image
+results = model('highway_image.jpg')
+
+# Process results
+for result in results:
+    boxes = result.boxes
+    for box in boxes:
+        x1, y1, x2, y2 = box.xyxy[0]
+        conf = box.conf[0]
+        cls = int(box.cls[0])
+        class_name = model.names[cls]
+        print(f"Detected: {class_name} (confidence: {conf:.2f})")
+```
+
+### Video Processing with Counting
+```python
+# Process video with vehicle counting
+results = model('traffic_video.mp4', save=True, save_txt=True)
+
+# The main.py script provides advanced counting and tracking features
+```
+
+### Using the Complete Application
+```python
+# Run the full application with counting and visualization
+from main import VehicleCounter
+
+counter = VehicleCounter()
+counter.process_video('input_video.mp4', 'output_video.mp4')
+```
+
+## Applications
+
+- Highway traffic monitoring
+- Vehicle counting and classification
+- Traffic flow analysis
+- Automated surveillance systems
+- Road safety monitoring
+- Traffic data collection
+
+## Related Repositories
+
+- **Full Dataset**: [highway-vehicle-detection-full](https://huggingface.co/datasets/bichuche0705/highway-vehicle-detection-full) - Complete project with datasets and videos
+- **Model Only**: [highway-vehicle-detection](https://huggingface.co/bichuche0705/highway-vehicle-detection) - Just the trained model
+
+## License
+
+MIT License - Free to use for research and commercial purposes
+
+## Contributing
+
+This is a research project. For questions or improvements, please contact the author.
+
+## Contact
+
+**Author**: Nguyen Quoc Viet  
+**Repository**: https://huggingface.co/bichuche0705/highway-vehicle-detection-code
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{highway-vehicle-detection-code,
+  title={Highway Vehicle Detection - Code \& Models},
+  author={Nguyen Quoc Viet},
+  year={2025},
+  url={https://huggingface.co/bichuche0705/highway-vehicle-detection-code}
+}
+```