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The following fruits and are included:
Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beetroot Red, Blueberry, Cactus fruit,
Cantaloupe (2 varieties), Carambula, Cauliflower, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened), Dates, Eggplant, Fig, Ginger
Root, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green,
Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties,
Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie,
Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon.
The total number of images: 90483.
Training set size: 67692 images (one fruit or vegetable per image).
Test set size: 22688 images (one fruit or vegetable per image).
The number of classes: 131 (fruits and vegetables).
Image size: 100x100 pixels.
Filename format: imageindex100.jpg (e.g. 32100.jpg) or rimageindex100.jpg (e.g. r32100.jpg) or r2imageindex100.jpg or r3imageindex100.jpg. "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels).
WARNING
There is a new -major version- of the dataset under release. A test archive (named fruits-360-original-size.zip) was already loaded to Kaggle. The new version contains images at their original (captured) size.
The name of the image files in the new version does not contain the "_100" suffix anymore. This will help you to make distinction between this version and the old 100x100 version.
So, if you use the 100x100 version, please make sure that the file names have the "_100" suffix. All others MUST be ignored.
END OF WARNING
Different varieties of the same fruit (apple for instance) are stored as belonging to different classes.
Fruits and vegetables were planted in the shaft of a low-speed motor (3 rpm) and a short movie of 20 seconds was recorded.
A Logitech C920 camera was used for filming the fruits. This is one of the best webcams available.
Behind the fruits, we placed a white sheet of paper as a background.
Here is a movie showing how the fruits and vegetables are filmed: https://youtu.be/_HFKJ144JuU
However, due to the variations in the lighting conditions, the background was not uniform and we wrote a dedicated algorithm that extracts the fruit from the background. This algorithm is of flood fill type: we start from each edge of the image and we mark all pixels there, then we mark all pixels found in the neighborhood of the already marked pixels for which the distance between colors is less than a prescribed value. We repeat the previous step until no more pixels can be marked.
All marked pixels are considered as being background (which is then filled with white) and the rest of the pixels are considered as belonging to the object.
The maximum value for the distance between 2 neighbor pixels is a parameter of the algorithm and is set (by trial and error) for each movie.
Pictures from the test-multiple_fruits folder were taken with a Nexus 5X phone.
Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Acta Univ. Sapientiae, Informatica Vol. 10, Issue 1, pp. 26-42, 2018.
The paper introduces the dataset and implementation of a Neural Network trained to recognize the fruits in the dataset.
This dataset is also available for download from GitHub: Fruits-360 dataset
Fruits were filmed at the dates given below (YYYY.MM.DD):
2017.02.25 - Apple (golden).
2017.02.28 - Apple (red-yellow, red, golden2), Kiwi, Pear, Grapefruit, Lemon, Orange, Strawberry, Banana.
2017.03.05 - Apple (golden3, Braeburn, Granny Smith, red2).
2017.03.07 - Apple (red3).
2017.05.10 - Plum, Peach, Peach flat, Apricot, Nectarine, Pomegranate.
2017.05.27 - Avocado, Papaya, Grape, Cherrie.
2017.12.25 - Carambula, Cactus fruit, Granadilla, Kaki, Kumsquats, Passion fruit, Avocado ripe, Quince.
2017.12.28 - Clementine, Cocos, Mango, Lime, Lychee.
2017.12.31 - Apple Red Delicious, Pear Monster, Grape White.
2018.01.14 - Ananas, Grapefruit Pink, Mandarine, Pineapple, Tangelo.
2018.01.19 - Huckleberry, Raspberry.
2018.01.26 - Dates, Maracuja, Plum 2, Salak, Tamarillo.
2018.02.05 - Guava, Grape White 2, Lemon Meyer
2018.02.07 - Banana Red, Pepino, Pitahaya Red.
2018.02.08 - Pear Abate, Pear Williams.
2018.05.22 - Lemon rotated, Pomegranate rotated.
2018.05.24 - Cherry Rainier, Cherry 2, Strawberry Wedge.
2018.05.26 - Cantaloupe (2 varieties).
2018.05.31 - Melon Piel de Sapo.
2018.06.05 - Pineapple Mini, Physalis, Physalis with Husk, Rambutan.
2018.06.08 - Mulberry, Redcurrant.
2018.06.16 - Hazelnut, Walnut, Tomato, Cherry Red.
2018.06.17 - Cherry Wax (Yellow, Red, Black).
2018.08.19 - Apple Red Yellow 2, Grape Blue, Grape White 3-4, Peach 2, Plum 3, Tomato Maroon, Tomato 1-4 .
2018.12.20 - Nut Pecan, Pear Kaiser, Tomato Yellow.
2018.12.21 - Banana Lady Finger, Chesnut, Mangostan.
2018.12.22 - Pomelo Sweetie.
2019.04.21 - Apple Crimson Snow, Apple Pink Lady, Blueberry, Kohlrabi, Mango Red, Pear Red, Pepper (Red, Yellow, Green).
2019.06.18 - Beetroot Red, Corn, Ginger Root, Nectarine Flat, Nut Forest, Onion Red, Onion Red Peeled, Onion White, Potato Red, Potato Red Washed, Potato Sweet, Potato White.
2019.07.07 - Cauliflower, Eggplant, Pear Forelle, Pepper Orange, Tomato Heart.
2019.09.22 - Corn Husk, Cucumber Ripe, Fig, Pear 2, Pear Stone, Tomato not Ripened, Watermelon.
MIT License
Copyright (c) 2017-2021 Mihai Oltean
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in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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