A food recognition Android app that tracks the users' diet and provides intelligent feedback to meet their personalized goals. I developed this on my own for my IB Computer Science Internal Assessment.
- Users can take pictures of their foods on the camera page within the app, and the app can recognize the foods from the pictures.
- The app can provide nutrition facts information of the meals.
- Users can input their body information, personalized calories goals, and time given for maintaining the diet.
- The app can calculate the recommended number of calories the users have left and update it whenever new meals are generated.
- The app can give appropriate suggestions to the user about the foods they should try based on personal preferences of certain types of foods and their calorie budgets.
The mobile application is built in Java for the Android mobile platform using the Android Studio IDE.
For food recognition, I used the Tensorflow library for mobile. To learn more check out the website:. The .
The nutrition facts for the foods are acquired through online by using the WolframAlpha API.
The food recognition model is created on top of the Inception Convolutional Neural Network Model. It trains and fits specifically on the Food-101 dataset by transfer learning using Tensorflow. The food dataset can be downloaded through this linkhere.
The CNN is trained on my laptop with processor i7-4720HQ, 12 GB of RAM, and graphics card GTX950M with 2 GB memory. It is trained for around 8 hours. I should have used a remote virtual machine with a more powerful CPU and GPU to train the neural network, but because of financial and time constraints, I trained it on my laptop for a baseline functional model.
The resulting accuracy ranges from 50% to 60% and can be increased should the model be trained for a longer period of time.
- Download and install Android Studio IDE.
- Clone this repo and import the project to Android Studio.