Requesting to assaign a issue

[LokeshYarramallu]

i am interested to contribution to your community in computer vision session under HACTOBERFEST
i am good at openCV and mediapipe so please asaign me a task
if any is ok i ahve few ideas to contribute i will

[Tinny-Robot]

Hi @LokeshYarramallu,

Thank you for your interest in contributing to our community during Hacktoberfest, and we appreciate your willingness to help in the computer vision session!

We would be delighted to have you contribute by creating a Jupyter notebook showcasing AI and ML with Python or R. Your notebook can cater to any level of expertise, whether you consider it suitable for beginners, intermediate users, experts, or professionals.

To ensure that your contribution aligns with our project's structure, we kindly request you to follow these guidelines:

1. Folder Structure: Please adhere to our project's folder structure. Create a new folder for your notebook under the relevant category (e.g., machine_learning, deep_learning, computer_vision, etc.).

2. Markdown Description: Include a meaningful Markdown description at the beginning of your notebook. This description should briefly explain the purpose of the notebook, the AI/ML techniques or concepts covered, and any specific goals or takeaways.

3. Data Description: If your notebook uses a dataset or specific data, provide a data description within the notebook. Include details about the data source, format, and any preprocessing steps performed.

4. Visualizations: Whenever applicable, incorporate visualizations (e.g., graphs, charts, plots) to enhance the understanding of the AI/ML concepts you're showcasing.

5. Contributor Guide: Consider adding a section or cell that provides a guide for contributors or users, especially if your notebook covers advanced topics.

Once you've created the notebook following these guidelines, please submit a pull request. We will review your contribution promptly.

If you have any questions or need assistance along the way, please don't hesitate to ask. We're here to support you in your contribution journey.

Additionally, if you find our project valuable, we'd greatly appreciate it if you could star the repository to show your support.

Thank you for your commitment to our community, and we look forward to seeing your notebook!

Best regards,
Tinny-Robot

[LokeshYarramallu]

sir i completed my my work
i made a jupiter notebook on pote hole detection training a VGG model through a kaggle dataset
i did it with detailed documentation
if you asaaign me this task ill pr my work
so please consider this comment