Fruit-Image-Dataset

The Fruit-Image-Dataset is an extensive collection designed for image classification projects, featuring a diverse array of fruits and vegetables. This dataset is perfect for researchers and developers aiming to train or test machine learning models in identifying various produce.

Dataset Details:

• Total Images: 90,483
• Training Set: 67,692 images, featuring one type of fruit or vegetable per image.
• Test Set: 22,688 images, comprising approximately 20% of the total dataset.
• Classes: 131 distinct fruits and vegetables.
• Image Naming Convention: Images are labeled using a unique index (e.g., 32_100.jpg), where “r” prefixes (like r_32_100.jpg) indicate rotations, and “100” denotes the image size (100×100 pixels).

Structure:

• Training Folder: Contains subfolders named after each fruit or vegetable, storing images used for model training.
• Test Folder: Similar to the training folder, it includes subfolders labeled by produce type, containing images used for model validation.