ML Nexus is an open-source collection of machine learning projects, covering topics like neural networks, computer vision, and NLP. Whether you're a beginner or expert, contribute, collaborate, and grow together in the world of AI. Join us to shape the future of machine learning!
Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is
Describe the solution you'd like
Develop a system to classify images of food items into categories (e.g., fruits, vegetables, desserts) for applications in nutrition tracking and restaurant menu management.
Steps:
Data Collection: Use existing datasets like Food-101 or gather your own images of various food items.
Model Selection: Implement a Convolutional Neural Network (CNN), potentially using transfer learning with pre-trained models like ResNet or MobileNet.
Model Training: Split the dataset into training, validation, and test sets; train the model while monitoring accuracy and loss.
Classification: identify different food items.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Na
Approach to be followed (optional)
A clear and concise description of the approach to be followed.
Na
Additional context
Add any other context or screenshots about the feature request here.
Na
Thanks for creating the issue in ML-Nexus!🎉
Before you start working on your PR, please make sure to:
⭐ Star the repository if you haven't already.
Pull the latest changes to avoid any merge conflicts.
Attach before & after screenshots in your PR for clarity.
Include the issue number in your PR description for better tracking.
Don't forget to follow @UppuluriKalyani – Project Admin – for more updates!
Tag @Neilblaze,@SaiNivedh26 for assigning the issue to you.
Happy open-source contributing!☺️
Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is
Describe the solution you'd like Develop a system to classify images of food items into categories (e.g., fruits, vegetables, desserts) for applications in nutrition tracking and restaurant menu management.
Steps:
Data Collection: Use existing datasets like Food-101 or gather your own images of various food items.
Model Selection: Implement a Convolutional Neural Network (CNN), potentially using transfer learning with pre-trained models like ResNet or MobileNet.
Model Training: Split the dataset into training, validation, and test sets; train the model while monitoring accuracy and loss.
Classification: identify different food items.
Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered. Na
Approach to be followed (optional) A clear and concise description of the approach to be followed. Na
Additional context Add any other context or screenshots about the feature request here. Na