SunnyAgarwal4274/Food_Ingredients

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

Dataset Details

Dataset Description

This dataset is a collection of high-quality images of fruits and vegetables, organized into distinct classes for effective training of machine learning models. It provides diverse representations of each category, allowing for accurate recognition and classification.

• Curated by: Sunny
• Language(s) (NLP): N/A
• License: cc-by-4.0

Dataset Sources [optional]

• Repository: https://www.kaggle.com/datasets/sunnyagarwal427444/food-ingredient-dataset-51

Uses

Direct Use

This dataset can be used for:

-Training image classification algorithms for recognizing fruits and vegetables. -Developing dietary apps that require food identification. -Conducting research in machine learning and computer vision.

Out-of-Scope Use

This dataset should not be used for:

-Misleading applications that misclassify or misrepresent food items. -Research involving sensitive personal data, as the dataset does not contain such information.

Dataset Structure

The dataset consists of images organized in subfolders, each named after the corresponding class (e.g., "Apples," "Carrots"). Each image file is labeled with the class name, making it easy to access and manage.

Dataset Creation

Curation Rationale

The dataset was created to provide a comprehensive resource for researchers and developers working on food recognition tasks, enabling advancements in agricultural technology and machine learning.

Source Data

Data Collection and Processing

Data was collected from various sources, including open-access image repositories and personal collections. Images were filtered to ensure quality, relevance, and diversity, with a focus on capturing different stages of ripeness and variations in appearance.

Who are the source data producers?

The source data was produced by various contributors, including researchers and enthusiasts in the field of agriculture and dietary science.

Annotations

Annotation process

Images were annotated manually by labeling each image with the appropriate class name. Annotation guidelines were developed to ensure consistency across the dataset.

Personal and Sensitive Information

The dataset does not contain personal or sensitive information, focusing solely on images of fruits and vegetables.

Bias, Risks, and Limitations

This dataset may exhibit biases based on the sources of images, which might not represent all varieties of fruits and vegetables globally. Users should be cautious when generalizing results from this dataset to broader contexts.

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Dataset Card Contact

Sunny Agarwal Email: agarwalsunny329@gmail.com