Images of various foods

AI Food Detection

## Overview

AI Food Detection is a dataset for object detection tasks - it contains Rice Soup Meat Porridge Egg annotations for 928 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).

Taiwanese Food Detection

## Overview

Taiwanese Food Detection is a dataset for object detection tasks - it contains Food annotations for 4,935 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).

Common Food Detection

## Overview

Common Food Detection is a dataset for object detection tasks - it contains Common Food annotations for 5,226 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).

A custom dataset was constructed for spoon food presence detection, containing manually labeled images across two categories: food present (Y) and no significant food (N).

Food Recognition Challenge

## Overview

Food Recognition Challenge is a dataset for object detection tasks - it contains Food annotations for 1,269 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).

This dataset presents a comprehensive Bengali food segmentation dataset designed to support both semantic segmentation and object detection tasks using deep learning techniques. The dataset consists of high-quality images of traditional Bengali dishes captured in diverse real-life settings, annotated with polygon-based masks and categorized into multiple food classes. Annotation and preprocessing were performed using the Roboflow platform, with exports available in both COCO and mask formats. The dataset was used to train UNet for segmentation and YOLOv12 for detection. Augmentation and class balancing techniques were applied to improve model generalization. This dataset provides a valuable benchmark for food recognition, dietary assessment, and culturally contextualized computer vision research.

NutraCal Food Detection

## Overview

NutraCal Food Detection is a dataset for object detection tasks - it contains Food annotations for 2,150 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).

The dataset was created with the task of fine - tuning a YOLO model to try how it will fare with two classes. All the bounding box labels are in the YOLOv8 format.

Dataset

This dataset was created by Nikhil Laddha

Contents

Hyperspectral imaging captures material-specific spectral data

Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognition technology has also rapidly extended to various fields, such as agricultural pests, disease identification, tumor marker recognition, wound severity judgment, road wear recognition, and food safety detection. This article proposes a non-wearable food recognition and weight estimation system (nWFWS) to identify the food type and food weight in the target recognition area via smartphones, so to assist clinical patients and physicians in monitoring diet-related health conditions. In addition, the system is mainly designed for mobile terminals; it can be installed on a mobile phone with an Android system or an iOS system. This can lower the cost and burden of additional wearable health monitoring equipment while also greatly simplifying the automatic estimation of food intake via mobile phone photography and image collection. Based on the system’s ability to accurately identify 1,455 food pictures with an accuracy rate of 89.60%, we used a deep convolutional neural network and visual-inertial system to collect image pixels, and 612 high-resolution food images with different traits after systematic training, to obtain a preliminary relationship model between the area of food pixels and the measured weight was obtained, and the weight of untested food images was successfully determined. There was a high correlation between the predicted and actual values. In a word, this system is feasible and relatively accurate for one automated dietary monitoring and nutritional assessment.

This is an open source object detection model by TensorFlow in TensorFlow Lite format. While it is not recommended to use this model in production surveys, it can be useful for demonstration purposes and to get started with smart assistants in ArcGIS Survey123. You are responsible for the use of this model. When using Survey123, it is your responsibility to review and manually correct outputs.This object detection model was trained using the Common Objects in Context (COCO) dataset. COCO is a large-scale object detection dataset that is available for use under the Creative Commons Attribution 4.0 License.The dataset contains 80 object categories and 1.5 million object instances that include people, animals, food items, vehicles, and household items. For a complete list of common objects this model can detect, see Classes.The model can be used in ArcGIS Survey123 to detect common objects in photos that are captured with the Survey123 field app. Using the modelFollow the guide to use the model. You can use this model to detect or redact common objects in images captured with the Survey123 field app. The model must be configured for a survey in Survey123 Connect.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputCamera feed (either low-resolution preview or high-resolution capture).OutputImage with common object detections written to its EXIF metadata or an image with detected objects redacted.Model architectureThis is an open source object detection model by TensorFlow in TensorFlow Lite format with MobileNet architecture. The model is available for use under the Apache License 2.0.Sample resultsHere are a few results from the model.

Multi Object Food Detection

## Overview

Multi Object Food Detection is a dataset for object detection tasks - it contains Foods annotations for 630 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).

Traditional Food Detection

## Overview

Traditional Food Detection is a dataset for object detection tasks - it contains Food annotations for 1,126 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).

Open Food Facts Nutriscore detection dataset

This dataset was used to train the Nutri-score object detection model running in production at Open Food Facts. Images were collected from the Open Food Facts database and labeled manually. Just like the original images, the images in this dataset are licensed under the Creative Commons Attribution Share Alike license (CC-BY-SA 3.0).

  Fields

image_id: Unique identifier for the image, generated from the barcode and the image… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/nutriscore-object-detection.

Open Food Facts Nutrition table detection dataset

This dataset was used to train the nutrition table object detection model running in production at Open Food Facts. Images were collected from the Open Food Facts database and labeled manually. Just like the original images, the images in this dataset are licensed under the Creative Commons Attribution Share Alike license (CC-BY-SA 3.0).

  Fields

image_id: Unique identifier for the image, generated from the barcode and… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/nutrition-table-detection.

About Dataset The file contains 24K unique figure obtained from various Google resources Meticulously curated figure ensuring diversity and representativeness Provides a solid foundation for developing robust and precise figure allocation algorithms Encourages exploration in the fascinating field of feed figure allocation

Unparalleled Diversity Dive into a vast collection spanning culinary landscapes worldwide. Immerse yourself in a diverse array of cuisines, from Italian pasta to Japanese sushi. Explore a rich tapestry of food imagery, meticulously curated for accuracy and breadth. Precision Labeling Benefit from meticulous labeling, ensuring each image is tagged with precision. Access detailed metadata for seamless integration into your machine learning projects. Empower your algorithms with the clarity they need to excel in food recognition tasks. Endless Applications Fuel advancements in machine learning and computer vision with this comprehensive dataset. Revolutionize food industry automation, from inventory management to quality control. Enable innovative applications in health monitoring and dietary analysis for a healthier tomorrow. Seamless Integration Seamlessly integrate our dataset into your projects with user-friendly access and documentation. Enjoy high-resolution images optimized for compatibility with a range of AI frameworks. Access support and resources to maximize the potential of our dataset for your specific needs.

Conclusion Embark on a culinary journey through the lens of artificial intelligence and unlock the potential of feed figure allocation with our SEO-optimized file. Elevate your research, elevate your projects, and elevate the way we perceive and interact with food in the digital age. Dive in today and savor the possibilities!

This dataset is sourced from Kaggle.

Food Detection Testing

## Overview

Food Detection Testing is a dataset for semantic segmentation tasks - it contains Food annotations for 244 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).

food recognition