Each year, approximately 6,78,000 deaths are caused in the United States of America due to unhealthy diet.
A typical American diet is too high in calories, fat, sugars, sodium, etc.
Hence, people have became more proactive when it comes to health matters.
Services like eating habit recorder and calorie/nutrition calculator have became extremely popular.
They can make users aware of problems like obesity, cancer, diabetes, heart-disease, etc. that can be caused by unhealthy diets.
Most of these services require the users to manually select a food item from a hierarchical menu which is a time consuming process and not so user friendly.
An user-interactive system that takes food images as an input, recognizes the food automatically and gives the nutritional-facts as an output will save a lot of time.
This system can be used in various areas such as social network, health-care applications, eating-habit evaluations, etc.
For food image recognition we will be using transfer learning to retrain the final layer (with 101 additional food-classes) of Inception-v3 model which is already trained by Google on 1000 classes.
It almost took 10-11 hours to train the model on Google Colab.
