In this project, I've implemented cheque detection using the YOLOv8 model. To train and evaluate the model, I have created a custom dataset consisting of 216 images of cheques, which have been manually annotated and labeled by myself using the Roboflow platform. The dataset is divided into three subsets as follows:

1. Training Set: 182 images
2. Validation Set: 23 images
3. Test Set: 11 images

The goal of this project is to detect the authenticity of cheques in images using the YOLOv8 object detection model. The project includes:

• A custom dataset of cheque images.
• Pre-trained YOLOv8 models.
• Scripts for running inference on new images and videos.
• Scripts for training the model on the custom dataset.

To run this project, you need to have Python 3.7+ and pip installed. Follow the steps below to set up the environment:

1. Clone the repository:

   git clone https://github.com/prabhleenn/Cheque-Detection.git
   cd Cheque-Detection

2. Install the required dependencies:

   pip install "https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml"

To run inference on images using the pre-trained (.pt) model, use the following commands:

• On an image:

  python detect.py --source path/to/your/image.jpg --weights path/to/yolov8/weights.pt

To train the model on the custom cheque dataset, follow these steps:

1. Ensure your dataset is in the YOLO format:

   dataset/
   ├── images/
   │ ├── train/
   │ │ ├── image1.jpg
   │ │ ├── image2.jpg
   │ │ └── ...
   │ ├── val/
   │ │ ├── image1.jpg
   │ │ └── ...
   │ └── test/
   │ ├── image1.jpg
   │ └── ...
   └── labels/
   ├── train/
   │ ├── image1.txt
   │ ├── image2.txt
   │ └── ...
   ├── val/
   │ ├── image1.txt
   │ └── ...
   └── test/
   ├── image1.txt
   └── ...
   

2. Update the yaml file with your dataset paths and parameters.

3. Run the training script:

   python train.py --data path/to/your/data.yaml --cfg path/to/yolov8/config.yaml --weights path/to/yolov8/weights.pt