NutriConv: Dataset adapted from EFSA PANCAKE project

This dataset has been adapted from the PANCAKE project developed by the European Food Safety Authority (EFSA), originally designed for dietary assessment in European populations. It contains 210 low-resolution images (182×136 px), each depicting a single food item with associated weight annotations.

To support the development and evaluation of multitask deep learning models for food classification and weight estimation, each image is labeled with:

• A food category identifier

• The corresponding food weight in grams

• A segmentation mask (PNG) generated using Meta’s Segment Anything Model (SAM), manually refined for pixel-level accuracy

This dataset was used in the article "NutriConv: A Convolutional Approach for Digital Dietary Tracking trained on EFSA’s PANCAKE Dataset". While the original PANCAKE data was not structured for machine learning, this version includes preprocessed, cleaned, and annotated images in a format suitable for deep learning workflows.

Contents:

• images/: Cleaned food images

• masks/: Segmentation masks in PNG format

• labels.csv: File containing image names, food class IDs, and weights in grams

Additionally, we include a subset of the Nutrition5k dataset, reorganized into classes based on unique sets of ingredients, disregarding their quantities. Only combinations appearing in at least ten images were retained, resulting in 896 images grouped into 44 ingredient-based classes. While this class definition introduces visual variability—since different dishes may share ingredients but differ in appearance—it provides a pragmatic approximation aligned with our classification task. This curated subset was used as an external validation set for evaluating the performance of the NutriConv model.

Thames, Q., Karpur, A., Norris, W., Xia, F., Panait, L., Weyand, T., & Sim, J. (2021). Nutrition5k: Towards automatic nutritional understanding of generic food. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8903-8911).