Objective: Build a machine learning model using ivy that can recognize ingredients in food images. This tool aims to assist individuals with dietary restrictions or allergies in quickly identifying ingredients in packaged foods, enhancing their dining safety and convenience.
Task Details:
Dataset: The project will utilize the Food-101 dataset, which is available at Food-101 Dataset. This dataset comprises images of 101 food categories, each accompanied by a list of ingredients, providing a rich foundation for developing an ingredient recognition model.
Expected Output: Contributors are required to submit a Jupyter notebook detailing the development of the ingredient recognition model. This includes steps such as data preprocessing, model training, and evaluation, with a focus on image processing techniques. Additionally, the submission should include the trained model files.
Submission Directory: Place your completed Jupyter notebook and model files in the Contributor_demos/Food Ingredient 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 Food Ingredient Recognition demo.
Develop your model, ensuring to document the methodology and findings comprehensively in the Jupyter notebook.
Save your notebook and model files in the Contributor_demos/Food Ingredient Recognition directory.
Push your branch to your forked repository once your work is complete.
Submit a Pull Request (PR) to the unifyai/demos repository, ensuring your PR title clearly indicates the project, such as "Food Ingredient Recognition Demo Submission".
Contribution Guidelines:
Your code should be well-documented to facilitate understanding and replication by others.
Summarize your approach, key insights, and any challenges you encountered in your PR description, providing a clear overview of your project journey.
Objective: Build a machine learning model using ivy that can recognize ingredients in food images. This tool aims to assist individuals with dietary restrictions or allergies in quickly identifying ingredients in packaged foods, enhancing their dining safety and convenience.
Task Details:
Dataset: The project will utilize the Food-101 dataset, which is available at Food-101 Dataset. This dataset comprises images of 101 food categories, each accompanied by a list of ingredients, providing a rich foundation for developing an ingredient recognition model.
Expected Output: Contributors are required to submit a Jupyter notebook detailing the development of the ingredient recognition model. This includes steps such as data preprocessing, model training, and evaluation, with a focus on image processing techniques. Additionally, the submission should include the trained model files.
Submission Directory: Place your completed Jupyter notebook and model files in the
Contributor_demos/Food Ingredient Recognitionsubdirectory within theunifyai/demosrepository.How to Contribute:
unifyai/demosrepository to your GitHub account.Contributor_demos/Food Ingredient Recognitiondirectory.unifyai/demosrepository, ensuring your PR title clearly indicates the project, such as "Food Ingredient Recognition Demo Submission".Contribution Guidelines: