Objective: Develop a machine learning model using ivy that classifies artwork into different artistic styles, such as impressionism, surrealism, and cubism, by analyzing images of paintings. This project is an engaging opportunity for art enthusiasts and historians to merge their passion for art with the power of machine learning, enhancing our understanding and appreciation of various art movements.
Task Details:
Dataset: The project will utilize the dataset available through the "Painter by Numbers" competition on Kaggle, which can be found here: Painter by Numbers Dataset. This dataset includes images of paintings from various artists and styles, providing a rich basis for developing and training your model.
Expected Output: Contributors are expected to submit a Jupyter notebook that documents the process of model development, including data preprocessing, feature extraction from images, model training, and evaluation. The trained model files should also be included with your submission.
Submission Directory: Your completed Jupyter notebook and model files should be placed in the Contributor_demos/Artwork Style Recognition subdirectory within the unifyai/demos repository.
How to Contribute:
Fork the unifyai/demos repository to your GitHub account.
Clone the forked repository to your local machine.
Create a new branch specifically for your work on the Artwork Style Recognition demo.
Proceed to develop your model, making sure to thoroughly document your methodology in the Jupyter notebook.
Place your completed notebook and model files in the Contributor_demos/Artwork Style Recognition directory.
Push your branch to your forked repository once you've completed your work.
Submit a Pull Request (PR) to the unifyai/demos repository, ensuring your PR title clearly reflects the project, such as "Artwork Style Recognition Demo Submission".
Contribution Guidelines:
Ensure your code is well-documented to facilitate ease of understanding and replication by others.
Provide a brief explanation in your PR description, summarizing your approach, any significant findings, and challenges faced during the project.
Objective: Develop a machine learning model using ivy that classifies artwork into different artistic styles, such as impressionism, surrealism, and cubism, by analyzing images of paintings. This project is an engaging opportunity for art enthusiasts and historians to merge their passion for art with the power of machine learning, enhancing our understanding and appreciation of various art movements.
Task Details:
Dataset: The project will utilize the dataset available through the "Painter by Numbers" competition on Kaggle, which can be found here: Painter by Numbers Dataset. This dataset includes images of paintings from various artists and styles, providing a rich basis for developing and training your model.
Expected Output: Contributors are expected to submit a Jupyter notebook that documents the process of model development, including data preprocessing, feature extraction from images, model training, and evaluation. The trained model files should also be included with your submission.
Submission Directory: Your completed Jupyter notebook and model files should be placed in the
Contributor_demos/Artwork Style Recognitionsubdirectory within theunifyai/demosrepository.How to Contribute:
unifyai/demosrepository to your GitHub account.Contributor_demos/Artwork Style Recognitiondirectory.unifyai/demosrepository, ensuring your PR title clearly reflects the project, such as "Artwork Style Recognition Demo Submission".Contribution Guidelines: