Month: March 2021

This week I focused on my work for the recommendation part as well as supporting tests for image recognition. I collected a set of images of the candidate items posted in the previous status report, and my teammate will use the image data set to test the performance of Google Vision on these candidate items.  Some example images are posted below.

For the recommendation part, I built two functionalities. The first function will accept an input including two keys, tags and ingredients. It will output a list of recipe names for users to select from. Some sample input and output is as follows:

Input:

test_request = {‘tag’: [‘non-dairy’];’ingredient’: [‘apple’, ‘orange’,’tuna’]}

Output:

[Apple and orange jam’]

If no recipe can be matched to the request, then it will return the following output:

[‘No results found’]

All samples above are the real inputs and outputs of the system.

Also, after the user selects the recipe they want to use, the next function will accept input of recipe index, and return a list of instructions. A sample output will be the following:

[‘Cut 2 apples and 2 oranges into small chunks’, ‘boil 200ml of water in a pot’,
‘add apples, oranges and 50g of sugar into the pot, and stew with medium heat’, ‘when the content becomes thick, turn off the heat and add 2 table spoons of lemon juice’]

The progress is on schedule. I will use the performance result from Google Vision to decide the 9 items and 30 recipes next week and generate more test cases to test on the recipe recommendations. Also, I am currently working on the email sending recipes to user functionality. There are some issues with the authentication, but I will try to figure it out next week.

 

 

 

Personally, I focused on the work of the design review report.  After reviewing more related works and analyzing the ingredients that might result in high recognition accuracy and lower false cases, I built a table of ingredient candidates that we might count as the final 10 items that we will be recognizing and relating recipes to. The table is attached in the post.

In addition, I learned about the current popular recommendation systems and how to build them from scratch. I followed these tutorials: https://www.toptal.com/algorithms/predicting-likes-inside-a-simple-recommendation-engine and https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-recommendation-engine-python/ to help me design the algorithms for a small scale recommendation system.

I am in the right place of the schedule we planned and not behind. For the deliverables for next week, we will first finish up the report together, and next personally I will be focusing on grabbing the recipes related to the candidates listed in the table and finalize our 10 items on the list and 30 recipes as well.

One of the most significant risks that could harm the success of the project is that we may have to work separately in order to make sure that different tasks can be tackled at the same pace. We will manage the risk by ordering two pieces of the same hardware parts so that we will be able to do our own work at the same time. The hardware parts will not be wasted because there is the possibility that we will add a second layer to our fridge so that we will use two hardware pieces.

Another risk that we currently met is that even though we order all parts using the form last Sunday and expected that the orders would have been placed on Tuesday, we did not know that we needed to inform TAs in order for the orders to be confirmed. Thus, we didn’t get the orders placed until Friday, which may influence our schedule. Fortunately, our models and codes will not be run on jetson Nano until we tested them on PC, so the delay in these parts will not be a big problem. For the acrylic boards and the LED grids, we will try to work on the building work together to shorten the time we need for this part of the work.

In general, we do not have any changes to our proposed schedule. This week we worked on the design review presentation together, and we tried to specify the implementations of the software part and support our ideas with related works and details. We also decided on the bill of materials and placed the orders. For the next week, we will work on the design review report and start working on our own tasks as planned.

 

This week I worked with my teammates on the design review presentation. Personally, I first worked on the schematic diagram. I simulated the ideal product in the real-world application by renovating my own fridge, taking a photo of this real fridge, and adding components to the photo(below shows the photo in the process; the final pic exceeds the 8Mb limit and will be displayed in presentation). In addition, I also sketched a diagram of how our final product for proof of concept will look like.

For the recommendation system, I read many related works available online and some popular implementation of recommendation system training networks. From all articles and works, I found a paper that focused on a cooking recipe recommendation system that runs on a consumer smartphone. https://online-journals.org/index.php/i-jim/article/view/3623/3116

I compared their applications of bag of features with my previous related projects to help me build my own training models.

I think we are on the schedule planned before. For the next week, we will first focus on finishing the design review report; if I have extra time, I will start building the baseline model by first implementing the feature extraction and test on the accuracy.