This repository contains the code for training and predicting prayer movements using the YOLOv11 model.

• Project Overview
• Model Training
• Model Prediction
• Requirements
• How to Use
• Acknowledgements

This project is focused on predicting prayer postures using a custom-trained YOLOv11 model. The model is trained to detect specific prayer postures from input images.

The model was trained on a custom dataset containing images of different prayer postures. The YOLOv11 framework was used for training.

To run predictions on images, the following command can be used:

!yolo task=detect mode=predict model='best.pt' conf=0.25 source='image.jpg' save=True

This command loads the best.pt model and predicts objects in the input image with a confidence threshold of 25%. The predictions are saved locally.

• Python 3.x
• PyTorch
• YOLOv11

You can install the required dependencies using:

pip install -r requirements.txt

1. Clone the repository:

   git clone https://github.com/alihassanml/Yolo11-Prayer-Prediction.git
   cd Yolo11-Prayer-Prediction

2. Ensure that your trained model file (best.pt) is in the root directory.

3. Run predictions using the provided script:

   !yolo task=detect mode=predict model='best.pt' conf=0.25 source='path_to_your_image' save=True

4. The output will be saved with the predictions drawn on the input image.

• This project utilizes the YOLOv11 model for object detection.
• Special thanks to the contributors of the YOLOv5 and YOLOv11 frameworks.

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