This week, I focused primarily on image classification rather than label reading. I looked through the Tesseract library and watched several videos on using it to classify images. Likewise, I explored using support vector machines to hand-make our own classification algorithm. This included a compilation of training data from Kaggle’s 2022 Food Recognition set (https://www.kaggle.com/datasets/sainikhileshreddy/food-recognition-2022). This training data can help for whatever method we chose to apply. Finally, I tested ChatGPT and its API more to potentially use it for classification and label reading. As a result, we have these three potential design paths that we could take. Each of these paths have their own tradeoffs that I have explored and written down below, and I hope to make a decision next week after doing more research.
1. Online Label Reading and Classification Library: The benefits are that all the hard work and algorithms are created. Likewise, the method has been tested and published online. All I need to do is import the libraries and write functions that call these libraries. This will reduce the design process tremendously. On the cons side, it will take a lot of preliminary research and understanding of other people’s code. Likewise, some of the code may be outdated and lack the accuracy we need for our project.
2. Incorporate Tesseract and Create Our Own Algorithm: The primary benefit of this is better top-down understanding of everything. Likewise, I can fine-tune it to fit our product specifically. Maintenance and product updates would be quicker since I will have full understanding of a majority of the code. The con is that this will take a lot of time upfront with learning everything and small design choices along the way. For example, I have looked into the tradeoffs between using Support Vector Machines vs Naive Bayes vs Trees. All of them have their unique choices, but I was able to study up on the math and principals behind each of these algorithms.
3. Chat GPT API: The benefit of this is reliability, precision, and speed. I queried various inputs and formatted the replies of Chat GPT to be compatible with key-word extraction to store values in the database. The accuracy was quite remarkable, and it could classify images as well as read labels. The main con is understanding the API and the potential to have a high cost. However, we are definitely interested in somehow incorporating Chat GPT into our product.
In addition to that, I helped with creating the design presentation. I focused primarily on the implementation plan, testing, and finalizing our schedule via the Gantt chart. I worked with my team members to create the slides and write the script for the presentation. I went through feedback from our first presentation to better design our next presentation and our product in general. Thanks again to all the students, TAs, and professors who provided commentary on it.
Regarding the special prompt we are supposed to answer for this report, I have decided to write part A. With respect to considerations of public health, safety and welfare, our solution is designed to improve physical well-being for users by monitoring caloric intake. Likewise, our product aims to reduce the psychological burden of tracking what and how much you eat as our product does all the tracking for you. Furthermore, keeping track and inventory of all the food you buy at the store can be a burden, and people are quite forgetful sometimes. Our product reduces the stress in remembering what is in your fridge or pantry by keeping count of every item. We have ambitious goals for our product to reduce over and under eating which aims to better the physical health and well-being of the public.
With respect to safety, our product will have limited hazards as it will consist of a sturdy physical structure with a scale, camera, and lights. The components will be as minimalistic as possible, and we will conduct rigorous testing to make sure there are no malfunctions. The lights will require as little power as possible and will be properly embedded into the walls. Likewise, the camera will be off whenever the scale reading is 0 as a result the product will only turn on when an item is put on the scale.