Cookpad Image Dataset

Abstract

In food-related services, image information is as important as text information for users. For example, in recipe search services, users find recipes based not only on text but also images. To promote studies on food images, many datasets have recently been published. However, they have the following three limitations: most of the datasets include only thousands of images, they only take account of images after cooking not during the cooking process, and the images are not linked to any recipes. In this study, we construct the Cookpad Image Dataset, a novel collection of food images taken from Cookpad, the largest recipe search service in the world. The dataset includes more than 1.64 million images after cooking, and it is the largest among existing datasets. Additionally, it includes more than 3.10 million images taken during the cooking process. To the best of our knowledge, there are no datasets that include such images. Furthermore, the dataset is designed to link to an existing recipe corpus and thus, a variety of recipe texts, such as the title, description, ingredients, and process, is available for each image. In this paper, we described our dataset's features in detail and compared it with existing datasets.

A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques

“…Researchers have used meal images, text description of meals or both (Multimodal) as input data to estimate nutrition of food. In some studies [34], for better performance results researchers have used text data along with the food images. However, our review is limited to input data with food images only.…”

“…Early datasets [37] have smaller numbers of food images than the recent ones [34], [46]. The datasets representing specific cuisines, e.g., Turkish food image dataset [3] also have a small number of food images.…”

“…The datasets representing specific cuisines, e.g., Turkish food image dataset [3] also have a small number of food images. The food image datasets such as [34], [35], and [46] that are mostly created from mixed cuisines e.g., English, Italian, Japanese, Korean, Indian, etc. have a large number of food images and food classes.…”

“…to collect food image data from users. Some [34] collected data from cooking websites. The additional information such as the food ingredients and the volume or calorie amount from the cooking websites may help researchers to improve the performance of the food nutrition system.…”

“…Some other articles have used a semi-automatic approach for nutritional value estimation where they need feedback from users. Therefore, for counting approximate calories from food images, researchers have taken the following two distinct approaches: 1) automated retrieval of the nutritional information from food nutrition databases [27], [34], and VOLUME 11, 2023 2) manual user input such as crowd sourcing using web platforms, smartphone apps, etc. [16], [103].…”