Various developed countries have experienced a continuous rise in obesity [1]. This can be burdensome on healthcare and has thus prompted governments to introduce laws and regulations to restaurants and food chains in an attempt to promote healthier eating [1]. One patent rule is the mandatory display of calories in menus, which has not come without an added cost to businesses. For example, Section 4205 of the Affordable Care Act (ACA) in the USA required certain food chains to display caloric information on menu items which was estimated to cost $315 million to comply [2]. These costs were primarily due to nutritional analysis - if one could drastically diminish this overhead, it would greatly lower business spending [2].

Calorie and nutrition research has attained increased interest in recent years. But, due to the complexity of the problem, literature in this area focuses on a limited subset of ingredients or dish types and simple convolutional neural networks or traditional machine learning. Simultaneously, estimation of ingredient portions can help improve calorie estimation and meal re-production from a given image. In this paper, given a single cooking image, a pipeline for calorie estimation and meal re-production for different servings of the meal is proposed. The pipeline contains two stages. In the first stage, a set of ingredients associated with the meal in the given image are predicted. In the second stage, given image features and ingredients, portions of the ingredients and finally the total meal calorie are simultaneously estimated using a deep transformer-based model. Portion estimation introduced in the model helps improve calorie estimation and is also beneficial for meal re-production in different serving sizes. To demonstrate the benefits of the pipeline, the model can be used for meal kits generation. To evaluate the pipeline, the large scale dataset Recipe1M is used. Prior to experiments, the Recipe1M dataset is parsed and explicitly annotated with portions of ingredients. Experiments show that using ingredients and their portions significantly improves calorie estimation. Also, a visual interface is created in which a user can interact with the pipeline to reach accurate calorie estimations and generate a meal kit for cooking purposes.