Project Title

This project involves data scraping, image preprocessing, and object detection using YOLO models, integrated into a web application with both frontend and backend functionalities. The primary goal is to detect car logos and brands from images and videos using deep learning models.

Table of Contents

• Directory Structure
• Installation
• Usage
• Dataset Preparation
• Model Training
• Web Application
• Contributing
• License

Directory Structure

internship/
│
├── new.py
├── Data_and_Model/
│   ├── Conversion_to_png/            # Stores preprocessed images from different notebooks
│   │   ├── Kia_grascal.ipynb
│   │   ├── Lexus_Mod.ipynb
│   │   └── ...                       # Other preprocessing scripts
│   ├── Datascraping/                 # Contains scripts for data scraping
│   │   ├── Scraptester1.ipynb
│   │   ├── script.py
│   │   ├── swift.ipynb
│   │   └── swift2.ipynb
│   ├── Upload_roboflow_to_anotate/   # Scripts for uploading images to Roboflow for annotation
│   │   └── Upload.ipynb
│   ├── Yolo/                         # YOLO model training and testing
│   │   ├── instl.ipynb
│   │   ├── yolov9_custom.ipynb
│   │   ├── yolov9/                   # YOLO repository with scripts and models
│   │   │   ├── benchmarks.py
│   │   │   ├── detect.py
│   │   │   ├── ...                   # Other scripts for detection and training
│   │   ├── CAR_LOGO_DETECTION_MODEL-1/
│   │   ├── CaRsdetection-2/
│   │   ├── ...                       # Additional model folders
│
├── main/                             # Web application
│   ├── Backend/                      # Backend scripts handling images and videos
│   │   ├── ImageBackend.py
│   │   ├── VideoBackend.py
│   │   ├── model.py
│   │   └── ...
│   ├── Frontend/                     # Frontend of the web app
│   │   ├── App/
│   │   │   ├── Front.py
│   │   │   ├── Video.py
│   │   │   └── image.py
│   │   └── ...
│
└── ...

Installation

1. Clone the repository:

   git clone <repository-url>
   cd project-directory

2. Install required dependencies:

   pip install -r requirements.txt

3. Set up the YOLO environment:

   • Follow the setup instructions provided in the instl.ipynb notebook.

Usage

Dataset Preparation

1. Data Scraping: Use the scripts in the Datascraping folder to gather images for training. These scripts scrape images of various car brands and save them into appropriate directories.

2. Image Preprocessing: The notebooks in the Conversion_to_png directory preprocess and convert the raw images into a format suitable for model training.

3. Annotation: Use the scripts in the Upload_roboflow_to_anotate folder to upload images to Roboflow, where they can be annotated for object detection tasks.

Model Training

• Training the Model:
  The Yolo/yolov9_custom.ipynb notebook is used to train the YOLOv9 model on the processed dataset. Ensure all dependencies are installed and paths to the datasets are correctly set.

• Testing the Model:
  Use detect.py and related scripts in the Yolo/yolov9 directory to test the trained models on new images or videos.

Web Application

• Backend:
  The backend scripts, located in the Backend directory, handle image and video uploads, preprocessing, and model inference.

• Frontend:
  The frontend scripts are in the Frontend directory and include web pages for image upload, video processing, and results display.

• Running the App:

  # Ensure all necessary services are running and configured
  python main.py

Contributing

Feel free to contribute by creating pull requests or raising issues. Make sure to follow the code style and include appropriate tests.

License

This project is licensed under the MIT License - see the LICENSE file for details.