Fruits and Vegetables Image Recognition Dataset

Overview

This dataset provides a diverse collection of fruits and vegetables images, curated for image recognition and machine learning tasks. It is an ideal resource for researchers, developers, and enthusiasts interested in food item identification and related applications.

Categories Included

The dataset covers a wide range of fruits and vegetables:

• Fruits: Banana, Apple, Pear, Grapes, Orange, Kiwi, Watermelon, Pomegranate, Pineapple, Mango
• Vegetables: Cucumber, Carrot, Capsicum, Onion, Potato, Lemon, Tomato, Radish, Beetroot, Cabbage, Lettuce, Spinach, Soybean, Cauliflower, Bell Pepper, Chilli Pepper, Turnip, Corn, Sweetcorn, Sweet Potato, Paprika, Jalapeño, Ginger, Garlic, Peas, Eggplant

This extensive variety supports the development of models capable of identifying diverse food items.

Dataset Structure

The dataset is organized into three folders, ensuring seamless integration into training workflows:

• Training Set: 100 images per category.
• Test Set: 10 images per category.
• Validation Set: 10 images per category.

Each folder contains subdirectories for individual fruit and vegetable types, ensuring organized and straightforward access to images.

Data Collection Methodology

The images were sourced using Bing Image Search for a personal project aimed at building an application for food item recognition. The purpose was to enable identification of ingredients and suggest recipes based on recognized food items.

• Legal Disclaimer:
  • The dataset is intended for educational and non-commercial use only.
  • Users are responsible for ensuring compliance with applicable copyright laws.
  • If any image owner has concerns regarding its inclusion, they are encouraged to contact the creator for removal.
  • The creator does not claim ownership of the images and assumes no liability for copyright-related issues.

Key Features

• Diverse Food Categories: Includes a broad range of fruits and vegetables to enable robust image recognition models.
• Well-Organized: Clear folder structure for training, testing, and validation phases.
• Ideal for AI Projects: Perfect for applications in food recognition, recipe generation, and educational projects in computer vision.
• User-Friendly: Ready-to-use dataset structure for machine learning pipelines.