---
license: cc-by-4.0
task_categories:
- image-classification
tags:
- Ingredients
- Food
- Fruits
- Vebetables
- Images
- CNN
- DL
pretty_name: 'Food Ingredients Dataset '
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
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]

<!-- Provide the basic links for the dataset. -->

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

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

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 section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

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

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

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

<!-- Motivation for the creation of this dataset. -->

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

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

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?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

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

### Annotations

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

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

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

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

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical 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

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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