PFID

Preparation of a dataset to train a food segmentation model

The Food Segmentation Dataset serves the tourism and visual entertainment sectors, consisting of a curated selection of internet-collected images with resolutions from 256 x 256 to 1024 x 768 pixels. This dataset is dedicated to contour segmentation, focusing on common foods and their accompanying plates or bowls, facilitating detailed analysis and representation in various applications.

Context

Semantic segmentation is the topic of interest among deep learning researchers in the recent era. It has many applications in different domains including, food recognition. In the case of food recognition, it removes the non-food background from the food portion. SEG-FOOD containing images of FOOD101, PFID, and Pakistani Food dataset and open-sourced the annotated dataset for future research. Images are annoated using JS Segment annotator.

Content

This dataset contains images from Food101, PFID, and Pakistani Food Dataset. The dataset is divided into training and testing with ground truth labels of the foods.

Acknowledgements

• Credits to Author G.Ahmed Tahir, Data Available on Github Repo.
• For more information about data Click here.

FoodSeg103 is a new food image dataset containing 7,118 images. Images are annotated with 104 ingredient classes and each image has an average of 6 ingredient labels and pixel-wise masks. It's provided as a large-scale benchmark for food image segmentation.

Major Challenges:

High intra-variance of the same food ingredient with different cooking methods Long-tail distribution Complicated contexts

Food Instance Segmentation

## Overview

Food Instance Segmentation is a dataset for instance segmentation tasks - it contains Food annotations for 1,325 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Food Instance Segmentation V1.0

## Overview

Food Instance Segmentation V1.0 is a dataset for instance segmentation tasks - it contains Food annotations for 1,412 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Dataset Card for FoodSeg103

  Dataset Summary

FoodSeg103 is a large-scale benchmark for food image segmentation. It contains 103 food categories and 7118 images with ingredient level pixel-wise annotations. The dataset is a curated sample from Recipe1M and annotated and refined by human annotators. The dataset is split into 2 subsets: training set, validation set. The training set contains 4983 images and the validation set contains 2135 images.

  Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/EduardoPacheco/FoodSeg103.

This dataset includes the images

SiddhantRout/Food-Segmentation-Sample_Images dataset hosted on Hugging Face and contributed by the HF Datasets community

Food

## Overview

Food is a dataset for instance segmentation tasks - it contains Food annotations for 499 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Food Segment & Classification 3

## Overview

Food Segment & Classification 3 is a dataset for semantic segmentation tasks - it contains Rice A2DB annotations for 266 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla crème and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing’s outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples.

Food Segment & Classification

## Overview

Food Segment & Classification is a dataset for semantic segmentation tasks - it contains Rice annotations for 266 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Setul de date privind segmentarea alimentelor deservește sectoarele turismului și divertismentului vizual, constând dintr-o selecție atent selecționată de imagini colectate de pe internet cu rezoluții de la 256 x 256 la 1024 x 768 pixeli. Acest set de date este dedicat segmentării contururilor, concentrându-se pe alimentele comune și farfuriile sau bolurile care le însoțesc, facilitând analiza detaliată și reprezentarea în diverse aplicații.

Datasættet for fødevaresegmentering betjener turisme- og visuel underholdningssektoren og består af et kurateret udvalg af internetindsamlede billeder med opløsninger fra 256 x 256 til 1024 x 768 pixels. Dette datasæt er dedikeret til kontursegmentering med fokus på almindelige fødevarer og deres tilhørende tallerkener eller skåle, hvilket letter detaljeret analyse og repræsentation i forskellige anvendelser.

Food Segment & Classification 2

## Overview

Food Segment & Classification 2 is a dataset for semantic segmentation tasks - it contains Rice UawZ annotations for 266 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Набор данных Food Segmentation Dataset обслуживает секторы туризма и визуальных развлечений, состоящие из тщательно подобранных изображений, собранных в Интернете, с разрешением от 256 x 256 до 1024 x 768 пикселей. Этот набор данных предназначен для контурной сегментации, фокусируясь на распространенных продуктах питания и сопровождающих их тарелках или мисках, что облегчает подробный анализ и представление в различных приложениях.

ABSTRACT We propose a segmentation algorithm for raisin extraction. The proposed approach consists of the following aspects. Deep learning is used to predict the number of raisins in each connected region, and the shape features such as the roundness, area, X-axis value for the centroid, Y-axis value for the centroid, axis length and perimeter of each region will be used to establish the prediction model. Morphological analysis, based on edge parameters including the polar axis, polar angle and angular velocity, is applied to search for the suitable break points that are useful for identifying the dividing lines between two adjacent raisins. To make our segmentation more accurate, some machine-learning algorithms such as the random forest (RF), support vector machine (SVM) and deep learning (deep neural network, DNN) are applied to predict the number of raisins and to decide whether the raisins need more segmentation. The performance of the three models is compared, and the DNN is the most accurate.

Bolivian Food For Segmentation

## Overview

Bolivian Food For Segmentation is a dataset for semantic segmentation tasks - it contains Foord annotations for 575 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).