Fruits-360 dataset: A dataset of images containing fruits, vegetables, nuts and seeds

Version: 2025.05.05.0

Content

The following fruits, vegetables and nuts 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), Beans, Beetroot Red, Blackberry, Blueberry, Cabbage, Caju seed, Cactus fruit, Cantaloupe (2 varieties), Carambula, Carrot, Cauliflower, Cherimoya, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened, regular), Dates, Eggplant, Fig, Ginger Root, Goosberry, 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), Pistachio, 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, Zucchini (green and dark).

Branches

The dataset has 5 major branches:

-The 100x100 branch, where all images have 100x100 pixels. See _fruits-360_100x100_ folder.

-The original-size branch, where all images are at their original (captured) size. See _fruits-360_original-size_ folder.

-The meta branch, which contains additional information about the objects in the Fruits-360 dataset. See _fruits-360_dataset_meta_ folder.

-The multi branch, which contains images with multiple fruits, vegetables, nuts and seeds. These images are not labeled. See _fruits-360_multi_ folder.

-The _3_body_problem_ branch where the Training and Test folders contain different (varieties of) the 3 fruits and vegetables (Apples, Cherries and Tomatoes). See _fruits-360_3-body-problem_ folder.

How to cite

Mihai Oltean, Fruits-360 dataset, 2017-

Dataset properties

For the 100x100 branch

Total number of images: 134605.

Training set size: 100912 images.

Test set size: 33693 images.

Number of classes: 196 (fruits, vegetables, nuts and seeds).

Image size: 100x100 pixels.

For the original-size branch

Total number of images: 54236.

Training set size: 27155 images.

Validation set size: 13579 images

Test set size: 13502 images.

Number of classes: 80 (fruits, vegetables, nuts and seeds).

Image size: various (original, captured, size) pixels.

For the 3-body-problem branch

Total number of images: 47033.

Training set size: 34800 images.

Test set size: 12233 images.

Number of classes: 3 (Apples, Cherries, Tomatoes).

Number of varieties: Apples = 29; Cherries = 12; Tomatoes = 19.

Image size: 100x100 pixels.

For the meta branch

Number of classes: 26 (fruits, vegetables, nuts and seeds).

For the multi branch

Number of images: 150.

Filename format:

For the 100x100 branch

image_index_100.jpg (e.g. 31_100.jpg) or

r_image_index_100.jpg (e.g. r_31_100.jpg) or

r?_image_index_100.jpg (e.g. r2_31_100.jpg)

where "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.

For the original-size branch

r?_image_index.jpg (e.g. r2_31.jpg)

where "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis.

The name of the image files in the new version does NOT contain the "_100" suffix anymore. This will help you to make the distinction between the original-size branch and the 100x100 branch.

For the multi branch

The file's name is the concatenation of the names of the fruits inside that picture.

Alternate download

The Fruits-360 dataset can be downloaded from:

Kaggle https://www.kaggle.com/moltean/fruits

GitHub https://github.com/fruits-360

How fruits were filmed

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 i...

This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.

Contents

Each sample includes:

3 10m resolution bands (RGB), 120px x 120px

1 10m resolution band (infrared), 120px x 120px

6 20m resolution bands, 60px x 60px

2 60m resolution bands, 20xp x 20px

1 pixel-level label map

2 binary masks for cloud and snow coverage

2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively

1 JSON-file containing additional meta-information

The meta.csv contains the following information about each sample:

Which season it belongs to

Which of the two grids it belongs to

Coordinates of the patch center

Whether it was acquired from Sentinel-2 Satellite A or B

Date and time of image acquisition

Snow and cloud coverage percentages

Image-level multi-class labels

Three additional image-level urbanization labels, based on the center pixel (details below)

The path to the sample

Classes

    ID
    Class




    1
    Continuous urban fabric


    2
    Discontinuous urban fabric


    3
    Industrial or commercial units


    4
    Road and rail networks and associated land


    5
    Port areas


    6
    Airports


    7
    Mineral extraction sites


    8
    Dump sites


    9
    Construction sites


    10
    Green urban areas


    11
    Sport and leisure facilities


    12
    Non-irrigated arable land


    13
    Vineyards


    14
    Fruit trees and berry plantations


    15
    Pastures


    16
    Broad-leaved forest


    17
    Coniferous forest


    18
    Mixed forest


    19
    Natural grasslands


    20
    Moors and heathland


    21
    Transitional woodland/shrub


    22
    Beaches, dunes, sands


    23
    Bare rock


    24
    Sparsely vegetated areas


    25
    Inland marshes


    26
    Peat bogs


    27
    Salt marshes


    28
    Intertidal flats


    29
    Water courses


    30
    Water bodies


    31
    Coastal lagoons


    32
    Estuaries


    33
    Sea and ocean

Urbanization classes

SLRAUM

0: None

1: Ländlicher Raum (~ rural area)

2: Städtischer Raum (~ urban area)

RTYP3

0: None

1: Ländliche Regionen (~ rural areas)

2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)

3: Städtische Regionen (~ urban areas)

KTYP4

0: None

1: Dünn besiedelte ländliche Kreise

2: Kreisfreie Großstädte

3: Ländliche Kreise mit Verdichtungsansätzen

4: Städtische Kreise

Further information on the urbanization classes can be found here:

SLRAUM

https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html

RTYP3

https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html

KTYP4

https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html

License of landcover model

Bundesamt für Kartographie und Geodäsie

dl-de/by-2-0 from https://www.govdata.de/dl-de/by-2-0

© GeoBasis-DE / BKG 2022

Source of landcover model

https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/

ben-ge/DEM: BigEarthNet Extended with Geographical and Environmental Data/Elevation Data

M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.

ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities:

* elevation data extracted from the Copernicus Digital Elevation Model GLO-30;
* land-use/land-cover data extracted from ESA Worldcover;
* climate zone information extracted from Beck et al. 2018;
* environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis;
* a seasonal encoding.

This archive contains the digital elevation model (DEM) data of ben-ge, which were extracted from the Copernicus Digital Elevation Model (GLO-30).

Data

Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground.

Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level.

Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch:
* patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches;
* patch_id_s1: the Sentinel-1 patch id for this specific patch;
* timestamp_s2: the timestamp for the Sentinel-2 observation;
* timestamp_s1: the timestamp for the Sentinel-1 observation;
* season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation;
* season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation;
* lon: longitude (WGS-84) of the center of the patch [degrees];
* lat: latitude (WGS-84) of the center of the patch [degrees];
* climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details).

File and directory structure

This archive contains the following directory and file structure:

|
|--- README (this file)
|--- ben-ge_meta.csv (ben-ge meta data)
|--- dem/ (digital elevation model data)
|--- S2A_MSIL2A_20171208T093351_3_82_dem.tif
...

To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/DEM requires 17.2 GB of space.

Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows:

|
|--- ben-ge_meta.csv (ben-ge meta data)
|--- ben-ge_era-5.csv (ben-ge environmental data)
|--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data)
|--- dem/ (digital elevation model data)
| |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif
| ...
|--- esaworldcover/ (land-use/land-cover data)
| |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif
| ...
|--- sentinel-1/ (Sentinel-1 SAR data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data)
| ...
|--- sentinel-2/ (Sentinel-2 multispectral data)
| |--- S2B_MSIL2A_20170818T112109_31_83/
| |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data)
| |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file)
...

More Information

For more information, please refer to https://github.com/HSG-AIML/ben-ge.

Citing ben-ge
If you use data contained in this archive, please cite the following paper:

M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.

MLRSNet is a multi-label high spatial resolution remote sensing dataset for semantic scene understanding. It provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.

The entire dataset is available as a huggingface dataset. In the form of Splits - https://huggingface.co/datasets/vigneshwar472/MLRS-Net-for-modelling In the form of Categories - https://huggingface.co/datasets/vigneshwar472/MLRS-Net

The 60 predefined class labels are aiplane, airport, bare soil, baseball diamond, basketball court, beach, bridge, buildings, cars, chaparral, cloud, containers, crosswalk, dense residential area, desert, dock, factory, field, football field, forest, freeway, golf course, grass, greenhouse, gully, habor, intersection, island, lake, mobile home, mountain, overpass, park, parking lot, parkway, pavement, railway, railway station, river, road, roundabout, runway, sand, sea, ships, snow, snowberg, sparse residential area, stadium, swimming pool, tanks, tennis court, terrace, track, trail, transmission tower, trees, water, wetland, wind turbine

The explanation of each directory can be found on data explorer.

Column descriptor of meta data files: The meta data files are available in labels directory and splits directory. Every metadata file has two colums - 1. image_id : id of images with which a user can fetch the corresponding .jpg file from corresponding folder 2. labels : all the labels associated with the image

Citation

Qi, Xiaoman; Zhu, Panpan; Wang, Yuebin; Zhang, Liqiang; Peng, Junhuan; Wu, Mengfan; Chen, Jialong; Zhao, Xudong; Zang, Ning; Mathiopoulos, P.Takis (2021), 
“MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding”, Mendeley Data, V3, doi: 10.17632/7j9bv9vwsx.3

Introduction The DeepLesion dataset contains 32,120 axial computed tomography (CT) slices from 10,594 CT scans (studies) of 4,427 unique patients. There are 1–3 lesions in each image with accompanying bounding boxes and size measurements, adding up to 32,735 lesions altogether. The lesion annotations were mined from NIH’s picture archiving and communication system (PACS). Some meta-data are also provided. The contents include: - Folder “Images_png”: png image files. We named each slice with the format “patient index_study index_series index_slice index.png”, with the last underscore being / or \ to indicate sub-folders. The images are stored in unsigned 16 bit. One should subtract 32768 from the pixel intensity to obtain the original Hounsfield unit (HU) values. We provide not only the key CT slice that contains the lesion annotation, but also its 3D context (30mm extra slices above and below the key slice). Due to the large size of the data and the file size limit o

Recording setup
Recordings have been taken place on 12th March 2019. Gaze data has been recorded with a Tobii 4C eye tracker with Pro license at 90 Hz. Resolution of the viewport was set to 1024x768. The display had a size of 24 inches and a resolution of 1680x1050 pixels. We polled the DOM tree every 50 milliseconds for fixed elements. We recorded the Web browsing of four participants, who followed the protocol as stored under "Dataset_visual_change/Instructions.doc".

Description of the dataset
The dataset consists of following three subsets.

1. Dataset_visual_change
The recordings of each participant p1-p4 on twelve Web sites are in the corresponding directories. For each Web site, there are nine to eleven files:

2. Dataset_stimuli
Stimulus shots and visual stimuli computed with the framework. Value-based, edge-based, signal-based, and SIFT-based features have been used. The labels of the first participant's session had been used to train a random forest classifier with 100 trees for visual change classification, using the named features. The discovery has been performed on each Web site from the dataset and
the results are placed in the respective directories. Inside each directory, there is one directory for the detected shots and one for the discovered stimuli. In the shots directory, there is one overview as

The shots have been merged to stimuli, which are placed in the stimuli directory. The stimuli are grouped per layer (scrollable, fixed elements, etc.) and meta information is available in

3. Dataset_evaluation
We have performed two evaluations of the visual stimuli discovery. One computational estimating the quality of stimuli. One case-study of an expert's task. There are two respective directories with the annotation data.

Changelog
[1.0.2] Add counts of layer pixels per participant.
[1.0.1] Change to CC0 license.
[1.0.1] Add labels of third annotator "l3".
[1.0.0] Initial release.