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Team Status Report for Apr 26 2025

Progress This Week

Our team made meaningful progress across hardware, cloud integration, and software components in preparation for the final project deadline

  • Computer Vision & Cloud Integration: The YOLOv5 model has been finalized and benchmarked for accuracy and inference speed on the Raspberry Pi, it is deployed through the cloud pipeline.

  • Hardware & Imaging: The motorized camera slider with stepper control and ring light is operational and is linked with the camera system for synchronized scanning.

  • Mobile Application & Recommendation System: The recipe recommendation system is functional and integrated into the app’s UI. It makes use of a query based system to return the top recommendations.
    Current Status

  • Our project is on schedule with all major components nearing full integration. Our efforts are focused on fine-tuning the system to ensure seamless interaction between all components in order to prepare for the final demo.

Goals for next week

  • Finalize camera and cloud transmission pipeline
  • Complete backend-mobile app communication for fridge scans
  • End-to-end testing across modules
  • Complete final deliverables
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Jun Wei’s Status Report for Apr 26 2025

1. Personal accomplishments for the week

1.1 Integration with cloud transmission pipeline

I have continued to work with Steven on integrating the motorized camera system with the cloud infrastructure he has been using for CV inference. There are some issues with getting transmitting the image to the cloud database in addition to latency constraints that need to be met.

1.2 Testing

I have been measuring the latencies of every stage of the image stitching and transmission pipeline, in addition to the stitching reliability rates.

2. Progress status

We are on track of tasks as we prepare for the final presentation for the coming week and continue with integration.

3. Goals for the upcoming week

  • Complete integration with cloud transmission pipeline (to work with Steven on this)

  • Complete integration with the smartphone application

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Steven’s Status Report for Apr 26 2025

For this week, I made the final touches to our detection model. I finalized training runs for our YOLOv5x model using the expanded dataset, and measured the benchmark accuracy as well as inference speed on the Raspberry Pi. I have also set up the cloud pipeline to integrate our model inference pipeline, in preparation for the final demo.

Overall, I am on schedule with our updated project timeline. The model has been finalized, and I am helping to complete the integration of the model with the cloud pipeline, the Raspberry Pi as well as our mobile application.

For next week, we will focus on completing the deliverables, finalizing the integration between our components in order to achieve a successful final demo.

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William’s Status Report for Apr 26 2025

Progress This Week

This week, I focused on putting the final finishing touches on the machine learning–based recipe recommendation system. The model now successfully generates recipe suggestions by comparing the user’s fridge contents and dietary preferences (converted into a query vector) against a database of recipe feature vectors based on ingredients and tags. The system scores similarity between these vectors and returns the top 5 most relevant matches.

I also completed integration of the recommendation system with the mobile app UI, ensuring that the results are displayed clearly and consistently. With this, the core functionality of the recommendation feature is now fully implemented and ready for demo.

Project Status

The project is on track to be completed within the next two days. The major components of the app—including user authentication, computer vision integration, backend data handling, and the recipe recommendation system—are now in place. Final testing and polishing are underway to ensure a smooth and cohesive user experience across all features.

Next Steps

  • Finish the project report and video

  • Prepare for final demo.

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Jun Wei’s Status Report for Apr 19 2025

1. Personal accomplishments for the week

1.1 Stitching algorithm integration

This week, I completed integration of my motorized camera system with the image stitching algorithm. The issue right now is the amount of time required for the image to successfully stitch, posing additional issues with meeting the overall pipeline time constraints.

1.2 Integration with cloud transmission pipeline

I have also begun working with Steven on integrating the motorized camera system with the cloud infrastructure he has been using for CV inference. There are some issues with getting transmitting the image to the cloud database.

2. Progress status

I am on schedule as the camera system has been integrated with the stitching algorithm. All that is left is multi-system integration.

3. Goals for the upcoming week

  • Complete integration with cloud transmission pipeline (to work with Steven on this)
  • Complete integration with the smartphone application.
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William’s Status Report for Apr 19 2025

This week, I pivoted from using LangChain to a machine learning–based approach for the recipe recommendation system. Instead of relying on prompt-based querying, I’m now representing each recipe as a feature vector derived from its ingredients and tags. Similarly, a user’s fridge contents and dietary preferences are converted into a query vector. Recommendations are generated by scoring the similarity between the query and each recipe vector, returning the top 5 most relevant matches.

In parallel, I made minor UI adjustments to improve the layout and clarity of the Preferences and Recipes tabs, helping prepare the app for future user testing.

Plans for Next Week

  • Finish implementing and testing the vector similarity scoring system across a sample recipe dataset.

  • Integrate the recommendation output with the mobile UI.

  • Explore options for caching or pre-processing recipe vectors for performance optimization.

This new approach gives us more control and flexibility over how recommendations are generated, and sets the stage for fine-tuning the system as we scale.

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Steven’s Status Report for Apr 19 2025

For this week, I deployed our YOLOv5 model onto the cloud and used it to run inference on fridge images. Furthermore, I refined our data upload pipeline, though there are some intermittent failures in the cloud upload service which requires more reliable error handling and try mechanisms.

Currently, we are on track and are finalizing the integration of our model with the Raspberry Pi and the cloud pipeline.

For next week, we will be conducting our final presentation. I will work to further improve cloud pipeline reliability and finalize integration between our components for our final demo.

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Team Status Report for Apr 12 2025

Team Status Report

Progress This Week

Our team made meaningful progress across hardware, cloud integration, and software components in preparation for the next stage of development.

  • Computer Vision & Cloud Integration: Steven worked on integrating the CV model and cloud components of the system. He successfully tested image upload and retrieval through the cloud pipeline and began preliminary integration of the YOLOv5 model for live inference. Training of the model is ongoing, and initial tests on the cloud system have been promising.

  • Hardware & Imaging: Jun Wei completed the construction and integration of the motorized camera slider with the updated camera hardware. The system has been mounted onto a smaller breadboard and tested within the mini fridge enclosure. He is now moving toward integrating the image stitching script with the Raspberry Pi system and preparing for cloud pipeline integration in collaboration with Steven.

  • Mobile Application & Recommendation System: Will implemented Google OAuth in the mobile app, enabling secure user sign-in and laying the groundwork for personalized features. He also started building the recipe recommendation system using LangChain, setting up the initial framework and exploring prompt designs to map fridge contents to relevant recipes.

Plans for Next Week

  • Finalize deployment of the YOLOv5 model for cloud inference and continue refining the cloud data pipeline (Steven).

  • Integrate image stitching into the Raspberry Pi workflow and begin transmission testing (Jun Wei).

  • Continue development of the recipe recommendation system and begin linking it with user preferences stored post-login (Will).

Overall, the team is on track with project milestones. Core components across hardware, CV, and app infrastructure are coming together, and collaboration between sub-teams is increasing as we begin cross-component integration.

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Jun Wei’s Status Report for Apr 12 2025

1. Personal accomplishments for the week

1.1 Motorized camera system integration

This week, I completed construction of the motorized camera slider and integration with the new camera obtained. I found a way to tether the movement of the timing belt to the camera baseplate using zip ties. I have also managed to move the system to a smaller breadboard and have tried placing it within the mini fridge that we will be using.

2. Progress status

I am on schedule as the motorized camera slider has been integrated with the camera system. I am now working on integrating the stitching algorithm script with the overall Raspberry Pi script.

3. Goals for the upcoming week

  • Integrate stitching algorithm
  • Integrate with cloud transmission pipeline (to work with Steven on this)
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William’s Status Report for Apr 12 2025

This week, I implemented Google OAuth integration into the mobile application, allowing users to sign in seamlessly and securely. This sets the foundation for saving personalized preferences tied to user accounts.

I also began working on the recipe recommendation system using LangChain. Initial efforts were focused on setting up the pipeline and experimenting with different prompt structures to connect user inputs with relevant recipe suggestions.

Plans for Next Week

  • Finish building out the recipe recommendation system and fine-tuning its performance.

  • Start linking user preferences to recipe suggestions post-login.

  • Explore potential improvements to the UI for the Preferences and Recipes tabs.

Progress this week has been steady, with key backend components starting to come together alongside the app frontend.