This repository contains a Jupyter Notebook helpers.ipynb that includes various helper functions for training object detection models using YOLO (You Only Look Once) architecture.

YOLO is a state-of-the-art object detection system that is widely used in computer vision applications. This notebook provides a collection of helper functions that will be useful for computer vision engineers who are working with YOLO.

This notebook includes the following helper functions:

• split_data: This function can be used to split the data to train and test folder and genrate a txt files for both train and test files , which can be used in yaml file for training purposes.
• image_preprocessing: This function is used to preprocess images for YOLO training. It includes resizing the images, converting them to the appropriate format, and normalizing the pixel values.
• annotation_parser: This function is used to parse annotation files and extract the bounding box information for each object in the image.
• generate_anchors: This function is used to generate anchor boxes for YOLO.
• evaluate: This function is used to evaluate the performance of the YOLO model on a given dataset.
• draw_boxes: This function is used to draw the predicted bounding boxes on an image.
• crop_image: This function helps to crop the image at the roi provided.
• create_class_names_file: This function helps to create the class_names.txt files for training purposes.

If you have any suggestions for additional helper functions that could be included in this notebook, please feel free to contribute. You can submit a pull request with your changes, and I will review them as soon as possible.

These helper functions are compatible with all versions of YOLO.

To use these helper functions, simply download the helpers.ipynb file and run it in a Jupyter Notebook environment. No additional installation is required.

This repository is licensed under the MIT License. Please see the LICENSE file for more information.

• Remember, with great YOLO power comes great object detection responsibility!