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