(This project haven't done yet) This repository contains code and resources for a traffic lamb recognition AI, built using basic AI models. The goal of this project is to develop an AI that can recognize traffic lambs in images, with the aim of passing the Google Captcha for traffic lamb recognition.
To use this AI, you will need to install [insert any required dependencies or packages]. You can do this using [insert installation method, such as pip or conda].
To use the AI, simply [insert instructions for running the code or integrating it with other software]. The AI takes in [insert input type, such as images or videos] and outputs [insert output type, such as labeled images or text files].
The traffic lamb recognition AI uses a basic AI model [insert type of model architecture, such as k-nearest neighbors or decision tree]. The model was trained on [insert dataset used for training, such as a custom dataset of traffic lamb images] using [insert training method, such as supervised learning].
The AI has been tested on [insert test dataset, such as a subset of the custom dataset] and achieved an accuracy of [insert accuracy percentage]. The model was also tested on the Google Captcha for traffic lamb recognition and successfully passed.
Contributions to this project are welcome! To contribute, simply [https://github.com/farukbeygo/artificial_intelligence_trafficlamb.git]. Please make sure to adhere to the project's coding standards and guidelines.
This project is licensed under the MIT License.
If you have any questions or feedback about this project, please feel free to contact [faruk.beygo@ug.bilkent.edu.tr].