A high-quality, dataset of images containing fruits. The following fruits are included: Apples (different varieties: Golden, Golden-Red, Granny Smith, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red), Cactus fruit, Cantaloupe (2 varieties), Carambula, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Clementine, Cocos, Dates, Granadilla, Grape (Pink, White, White2), Grapefruit (Pink, White), Guava, Huckleberry, Kiwi, Kaki, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine, Orange, Papaya, Passion fruit, Peach, Pepino, Pear (different varieties, Abate, Monster, Williams), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum, Pomegranate, Quince, Rambutan, Raspberry, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red), Walnut.
Total number of images: 55244.
Training set size: 41322 images (one fruit per image).
Test set size: 13877 images (one fruit per image).
Multi-fruits set size: 45 images (more than one fruit (or fruit class) per image)
Number of classes: 80 (fruits).
Image size: 100x100 pixels.
Filename format: image_index_100.jpg (e.g. 32_100.jpg) or r_image_index_100.jpg (e.g. r_32_100.jpg) or r2_image_index_100.jpg. "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels).
Different varieties of the same fruit (apple for instance) are stored as belonging to different classes.
Folders Training and Test contain all images used for training and testing.
Folder test-multiple_fruits contains images with multiple fruits. Some of them are partially covered by other fruits. Also, they were captured in different lighting conditions compared to the fruits from Training and Test folder. This is an excelent test for real-world detection.
Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Acta Univ. Sapientiae, Informatica Vol. 10, Issue 1, pp. 26-42, 2018.
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