metadata

language:
  - en
license: apache-2.0
size_categories:
  - 100M<n<1B
task_categories:
  - feature-extraction
  - text-classification
tags:
  - biomedical
  - imaging
  - computer vision
  - tuberculosis
  - multimodal
dataset_info:
  features:
    - name: FileName
      dtype: string
  splits:
    - name: train
      num_bytes: 70088819.588
      num_examples: 6284
  download_size: 42809832
  dataset_size: 70088819.588
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/Labels_*

DeepFruit Dataset

Dataset Details

This dataset contains total of 21,122 fully labeled images, featuring 20 different kinds of fruits. It is structured into an 80% training set (16,899 images) and a 20% testing set (4,223 images), facilitating a ready-to-use framework for model training and evaluation.

Additionally, there are two CSV files that label the types of fruits depicted in each image.

Dataset Description

The "DeepFruit" dataset is a comprehensive collection designed for the advancement of research in fruit detection, recognition, and classification. It encompasses a wide array of applications, including but not limited to, fruit recognition systems and calorie estimation. A total of 21,122 fully labeled images, featuring 20 different kinds of fruits. It is structured into an 80% training set (16,899 images) and a 20% testing set (4,223 images), facilitating a ready-to-use framework for model training and evaluation. This dataset provides a valuable resource for researchers aiming to develop automated systems leveraging deep learning, computer vision, and machine learning techniques for fruit image analysis.

Language(s): en
License: Mendeley License: CC BY 4.0

Dataset Sources

Data: https://data.mendeley.com/datasets/5prc54r4rt/1

Paper: https://www.sciencedirect.com/science/article/pii/S2352340923006248#sec0003

Uses

Convert Fruit Dataset From Image to PIL.

Direct Use

This section describes suitable use cases for the dataset.

Dataset Structure

"Train" & "Test": Datasets

"image_id": datasets.Value("string")

"number":datasets.Value("int32")

"image": datasets.Image()

"image_path": datasets.Value("string")

"label": datasets.Value("string")

Curation Rationale

It lies in its foundational role for enabling advanced machine learning applications in dietary and health management. By converting fruit images to the PIL format, it prepares data for analysis that could lead to innovations in recognizing and understanding fruit characteristics. This groundwork is crucial for developing technologies that assist in dietary planning, nutritional education, and managing health conditions through better food choices, thereby having a broad positive effect on public health and awareness.

Data Collection and Processing

 Image Format: All images are expected to be in JPEG format. Non-JPEG files are excluded during the data processing phase, ensuring consistency in file format.
 
 Label Extraction: Labels are extracted from separate CSV files (Labels_Train.csv and Labels_Test.csv), which map image names to their corresponding fruit labels. This method ensures that labels are organized and accessible.
 
 Data Splitting: The dataset is split into training and testing sets, as indicated by the separate ZIP files for train and test data. This standard practice facilitates the evaluation of model performance on unseen data.
 
 Python Imaging Library (PIL): Used for opening and manipulating images in the Python Imaging Library format. This choice is made for its wide adoption and ease of integration with other Python libraries for data science and machine learning tasks.
 
 Datasets Library from Hugging Face: Facilitates the creation, distribution, and loading of the dataset. This library provides a standardized way to work with datasets, including features for splitting, processing, and accessing dataset information.

Supported Tasks

The fruit images were captured under various conditions, including different plate sizes, shapes, and situations, as well as varying angles, brightness levels, and distances.

Foundation for Advanced ML Models/ Algorithms Training: By converting the fruit dataset into PIL format, we ensure that the data is in a uniform, accessible format that is compatible with various machine learning and deep learning libraries. This standardization is vital for the efficient training, validation, and testing of different classification models.
Enables Comprehensive Analysis: The dataset, featuring a wide variety of fruit images, is essential for developing a deep understanding of fruit characteristics. This includes not only basic identification but also detailed analyses such as sugar content, calorie count, and vitamin composition, which are crucial for dietary planning and health management.
Basis for Practical Applications: The dataset's conversion and subsequent use in machine learning model training are not academic exercises but are intended for real-world applications. The insights gained from this project could significantly impact dietary planning, particularly for individuals with specific health considerations like diabetes, by providing accurate, detailed information about fruit characteristics.

Bias, Risks, and Limitations

 Representation Bias: Given the dataset comprises 20 diverse fruit types across 8 combinations, there might be an underrepresentation of certain fruits, particularly those that are less common or indigenous to specific regions. This could lead to a model trained on this dataset performing less accurately on fruit types or varieties not included or underrepresented.
 
 Misclassification Risk: In critical applications where accurate fruit identification is crucial (e.g., dietary management apps, agricultural sorting mechanisms), misclassification could lead to adverse outcomes. This risk is heightened if the dataset contains mislabeled examples or if the model struggles with fruits that have similar appearances.
 
 Scope of Application: The dataset's utility is primarily confined to the domain of fruit recognition and classification. It may not be suitable for more nuanced tasks within agricultural technology, such as detecting fruit diseases or assessing ripeness, unless supplemented with additional, specialized data.