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 identifierThe corresponding food weight in gramsA segmentation mask (PNG) generated using Meta’s Segment Anything Model (SAM), manually refined for pixel-level accuracyThis 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 imagesmasks/: Segmentation masks in PNG formatlabels.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).

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 identifierThe corresponding food weight in gramsA segmentation mask (PNG) generated using Meta’s Segment Anything Model (SAM), manually refined for pixel-level accuracyThis 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 imagesmasks/: Segmentation masks in PNG formatlabels.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).

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 identifierThe corresponding food weight in gramsA segmentation mask (PNG) generated using Meta’s Segment Anything Model (SAM), manually refined for pixel-level accuracyThis 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 imagesmasks/: Segmentation masks in PNG formatlabels.csv: File containing image names, food class IDs, and weights in grams

DescriptionThis dataset contains restaurant reviews from TripAdvisor for five European cities, capturing detailed information on users, restaurants (items), and reviews. It offers a comprehensive view of user experiences, opinions, and restaurant attributes.Data StructureUser InformationuserId: Unique identifier for each user (hashed).name: Display name or username.location: User's location (city and country).Restaurant Information (Items)itemId: Unique identifier for each restaurant.name: Restaurant name.city: City where the restaurant is located.priceInterval: Price range.url: Link to the restaurant’s TripAdvisor review page.rating: Average rating score for the restaurant.type: List of cuisine types (e.g., [Spanish, Mediterranean]).Review InformationreviewId: Unique identifier for each review.userId: Corresponding user who wrote the review.itemId: Restaurant associated with the review.title: Title of the review summarizing the user’s impression.text: Full text of the review describing the user’s experience.date: Date when the review was posted.rating: Numerical score (typically from 0 to 50, where 50 represents the highest satisfaction).language: Language of the review.images: List of URLs pointing to images uploaded by the user (if available).url: Link to the full review on TripAdvisor.Code exampleimport pandas as pdcity = "Barcelona"# Load restaurantsitems = pd.read_pickle(f"{city}/items.pkl")# Load usersusers = pd.read_pickle(f"{city}/users.pkl")# Load reviewsreviews = pd.read_pickle(f"{city}/reviews.pkl")

DescriptionThis dataset contains restaurant reviews from TripAdvisor for five European cities, capturing detailed information on users, restaurants (items), and reviews. It offers a comprehensive view of user experiences, opinions, and restaurant attributes.Data StructureUser InformationuserId: Unique identifier for each user (hashed).name: Display name or username.location: User's location (city and country).Restaurant Information (Items)itemId: Unique identifier for each restaurant.name: Restaurant name.city: City where the restaurant is located.priceInterval: Price range.url: Link to the restaurant’s TripAdvisor review page.rating: Average rating score for the restaurant.type: List of cuisine types (e.g., [Spanish, Mediterranean]).Review InformationreviewId: Unique identifier for each review.userId: Corresponding user who wrote the review.itemId: Restaurant associated with the review.title: Title of the review summarizing the user’s impression.text: Full text of the review describing the user’s experience.date: Date when the review was posted.rating: Numerical score (typically from 0 to 50, where 50 represents the highest satisfaction).language: Language of the review.images: List of URLs pointing to images uploaded by the user (if available).url: Link to the full review on TripAdvisor.Code exampleimport pandas as pdcity = "Barcelona"# Load restaurantsitems = pd.read_pickle(f"{city}/items.pkl")# Load usersusers = pd.read_pickle(f"{city}/users.pkl")# Load reviewsreviews = pd.read_pickle(f"{city}/reviews.pkl")

Dataset of restaurant reviews from TripAdvisor that includes text and images from different cities.

Dataset with Point of Interest (POI) reviews from TripAdvisor that includes text and images from different cities.

Dataset with Point of Interest (POI) reviews from TripAdvisor that includes text and images from different cities.