Update README.md

Author: hamdiboukamcha

README.md

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-# Yolo-V11-cpp-TensorRT
-  The YOLOv11 C++ TensorRT Project in C++ and optimized using NVIDIA TensorRT 
+# YOLOv11 C++ TensorRT
+
+![Inference Time of YOLOv11 ](asset/output.gif)
+
+<a href="https://github.com/hamdiboukamcha/Yolo-V11-cpp-TensorRT" style="margin: 0 2px;">
+    <img src="https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub" alt="GitHub">
+</a>
+
+<a href="https://github.com/yourusername/YOLOv11-TensorRT/blob/main/LICENSE" style="margin: 0 2px;">
+    <img src="https://img.shields.io/badge/License-MIT-lightgreen?style=flat&logo=License" alt="License">
+</a>
+
+## 📜 Citation
+
+The **YOLOv11 C++ TensorRT Project** is a high-performance object detection solution implemented in **C++** and optimized using **NVIDIA TensorRT**. This project leverages the YOLOv11 model to deliver fast and accurate object detection, utilizing TensorRT to maximize inference efficiency and performance.
+
+---
+
+## 📢 Updates
+
+### Key Features:
+- **Model Conversion**: Convert ONNX models to TensorRT engine files to accelerate inference.
+- **Inference on Videos**: Efficiently perform object detection on video files.
+- **Inference on Images**: Execute object detection on individual images.
+- **High Efficiency**: Optimized for real-time object detection using NVIDIA GPUs.
+- **Preprocessing with CUDA**: CUDA-enabled preprocessing for faster input handling.
+
+![Benchmark Inference Time of YOLOv11 Models](asset/Bench_YOLO_V11.jpg)
+
+---
+## 📂 Project Structure
+  
+    YOLOv11-TensorRT/
+    ├── CMakeLists.txt          # Build configuration for the project
+    ├── include/                # Header files
+    ├── src/
+    │   ├── main.cpp            # Main entry point for the application
+    │   ├── yolov11.cpp         # YOLOv11 implementation
+    │   └── preprocess.cu       # CUDA preprocessing code
+    ├── assets/                 # Images and benchmarks for README
+    └── build/                  # Compiled binaries
+
+## 🛠️ Setup
+
+### Prerequisites
+
+- **CMake** (version 3.18 or higher)
+- **TensorRT** (V8.6.1.6: For optimized inference with YOLOv11.)
+- **CUDA Toolkit** (V11.7: For GPU acceleration)
+- **OpenCV** (V4.10.0: For image and video processing)
+- **NVIDIA GPU** (with compute capability 7.5 or higher)
+
+### Installation
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/hamdiboukamcha/Yolo-V11-cpp-TensorRT.git
+   cd YOLOv11-TensorRT
+2. Update the TensorRT and OpenCV paths in CMakeLists.txt:
+   ```bash
+   set(TENSORRT_PATH "F:/Program Files/TensorRT-8.6.1.6")  # Adjust this to your path
+
+4. Build the project:
+    ```bash
+    mkdir build
+    cd build
+    cmake ..
+    make -j$(nproc)
+## 🚀 Usage
+
+### Convert Yolov11 To ONNX Model
+    from ultralytics import YOLO
+    Load the YOLO model
+    model = YOLO("yolo11s.pt")
+    #Export the model to ONNX format
+    export_path = model.export(format="onnx")
+
+### Convert ONNX Model to TensorRT Engine
+
+To convert an ONNX model to a TensorRT engine file, use the following command:
+
+    ./YOLOv11TRT convert path_to_your_model.onnx path_to_your_engine.engine.
+        
+path_to_your_model.onnx: Path to the ONNX model file.
+
+path_to_your_engine.engine: Path where the TensorRT engine file will be saved.
+
+### Run Inference on Video
+To run inference on a video, use the following command:
+
+    ./YOLOv11TRT infer_video path_to_your_video.mp4 path_to_your_engine.engine
+
+path_to_your_video.mp4: Path to the input video file.
+
+path_to_your_engine.engine: Path to the TensorRT engine file.
+
+### Run Inference on Video
+Run Inference on Image
+To run inference on an image, use the following command:
+
+    ./YOLOv11TRT infer_image path_to_your_image.jpg path_to_your_engine.engine
+    
+path_to_your_image.jpg: Path to the input image file.
+
+path_to_your_engine.engine: Path to the TensorRT engine file.
+
+## ⚙️ Configuration
+
+### CMake Configuration
+In the CMakeLists.txt, update the paths for TensorRT and OpenCV if they are installed in non-default locations:
+
+#### Set the path to TensorRT installation
+
+    #Define the path to TensorRT installation
+    set(TENSORRT_PATH "F:/Program Files/TensorRT-8.6.1.6")  # Update this to the actual path for TensorRT
+    
+Ensure that the path points to the directory where TensorRT is installed.
+
+### Troubleshooting
+Cannot find nvinfer.lib: Ensure that TensorRT is correctly installed and that nvinfer.lib is in the specified path. Update CMakeLists.txt to include the correct path to TensorRT libraries.
+
+Linker Errors: Verify that all dependencies (OpenCV, CUDA, TensorRT) are correctly installed and that their paths are correctly set in CMakeLists.txt.
+
+Run-time Errors: Ensure that your system has the correct CUDA drivers and that TensorRT runtime libraries are accessible. Add TensorRT’s bin directory to your system PATH.
+
+## 📞 Contact
+
+For advanced inquiries, feel free to contact me on LinkedIn: <a href="https://www.linkedin.com/in/hamdi-boukamcha/" target="_blank"> <img src="assets/blue-linkedin-logo.png" alt="LinkedIn" width="32" height="32"></a>
+
+## 📜 Citation
+
+If you use this code in your research, please cite the repository as follows:
+
+        @misc{boukamcha2024yolov11,
+            author = {Hamdi Boukamcha},
+            title = {Yolo-V11-cpp-TensorRT},
+            year = {2024},
+            publisher = {GitHub},
+            howpublished = {\url{https://github.com/hamdiboukamcha/Yolo-V11-cpp-TensorRT/}},
+        }
+
+
+  
+
+
+