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--- |
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viewer: false |
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annotations_creators: |
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- expert-generated |
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language: [] |
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language_creators: |
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- expert-generated |
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license: |
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- cc-by-nc-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: "Embrapa Wine Grape Instance Segmentation Dataset \u2013 Embrapa WGISD " |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- original |
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tags: |
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- agriculture |
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- viticulture |
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- fruit detection |
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task_categories: |
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- object-detection |
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task_ids: [] |
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--- |
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Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD |
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================================================================ |
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[](https://zenodo.org/badge/latestdoi/199083745) |
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This is a detailed description of the dataset, a |
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*datasheet for the dataset* as proposed by [Gebru *et al.*](https://arxiv.org/abs/1803.09010) |
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Motivation for Dataset Creation |
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------------------------------- |
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### Why was the dataset created? |
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Embrapa WGISD (*Wine Grape Instance Segmentation Dataset*) was created |
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to provide images and annotation to study *object detection and instance |
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segmentation* for image-based monitoring and field robotics in |
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viticulture. It provides instances from five different grape varieties |
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taken on field. These instances shows variance in grape pose, |
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illumination and focus, including genetic and phenological variations |
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such as shape, color and compactness. |
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### What (other) tasks could the dataset be used for? |
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Possible uses include relaxations of the instance segmentation problem: |
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classification (Is a grape in the image?), semantic segmentation (What |
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are the "grape pixels" in the image?), object detection (Where are |
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the grapes in the image?), and counting (How many berries are there |
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per cluster?). The WGISD can also be used in grape variety |
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identification. |
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### Who funded the creation of the dataset? |
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The building of the WGISD dataset was supported by the Embrapa SEG |
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Project 01.14.09.001.05.04, *Image-based metrology for Precision |
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Agriculture and Phenotyping*, and the CNPq PIBIC Program (grants |
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161165/2017-6 and 125044/2018-6). |
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Dataset Composition |
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------------------- |
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### What are the instances? |
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Each instance consists in a RGB image and an annotation describing grape |
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clusters locations as bounding boxes. A subset of the instances also |
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contains binary masks identifying the pixels belonging to each grape |
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cluster. Each image presents at least one grape cluster. Some grape |
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clusters can appear far at the background and should be ignored. |
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### Are relationships between instances made explicit in the data? |
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File names prefixes identify the variety observed in the instance. |
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| Prefix | Variety | |
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| --- | --- | |
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| CDY | *Chardonnay* | |
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| CFR | *Cabernet Franc* | |
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| CSV | *Cabernet Sauvignon*| |
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| SVB | *Sauvignon Blanc* | |
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| SYH | *Syrah* | |
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### How many instances of each type are there? |
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The dataset consists of 300 images containing 4,432 grape clusters |
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identified by bounding boxes. A subset of 137 images also contains |
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binary masks identifying the pixels of each cluster. It means that from |
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the 4,432 clusters, 2,020 of them presents binary masks for instance |
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segmentation, as summarized in the following table. |
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|Prefix | Variety | Date | Images | Boxed clusters | Masked clusters| |
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| --- | --- | --- | --- | --- | --- | |
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|CDY | *Chardonnay* | 2018-04-27 | 65 | 840 | 308| |
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|CFR | *Cabernet Franc* | 2018-04-27 | 65 | 1,069 | 513| |
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|CSV | *Cabernet Sauvignon* | 2018-04-27 | 57 | 643 | 306| |
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|SVB | *Sauvignon Blanc* | 2018-04-27 | 65 | 1,316 | 608| |
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|SYH | *Syrah* | 2017-04-27 | 48 | 563 | 285| |
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|Total | | | 300 | 4,431 | 2,020| |
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*General information about the dataset: the grape varieties and the associated identifying prefix, the date of image capture on field, number of images (instances) and the identified grapes clusters.* |
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#### Contributions |
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Another subset of 111 images with separated and non-occluded grape |
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clusters was annotated with point annotations for every berry by F. Khoroshevsky and S. Khoroshevsky ([Khoroshevsky *et al.*, 2021](https://doi.org/10.1007/978-3-030-65414-6_19)). Theses annotations are available in `test_berries.txt` , `train_berries.txt` and `val_berries.txt` |
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|Prefix | Variety | Berries | |
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| --- | --- | --- | |
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|CDY | *Chardonnay* | 1,102 | |
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|CFR | *Cabernet Franc* | 1,592 | |
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|CSV | *Cabernet Sauvignon* | 1,712 | |
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|SVB | *Sauvignon Blanc* | 1,974 | |
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|SYH | *Syrah* | 969 | |
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|Total | | 7,349 | |
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*Berries annotations by F. Khoroshevsky and S. Khoroshevsky.* |
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Geng Deng ([Deng *et al.*, 2020](https://doi.org/10.1007/978-3-030-63820-7_66)) |
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provided point-based annotations for berries in all 300 images, summing 187,374 berries. |
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These annotations are available in `contrib/berries`. |
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Daniel Angelov (@23pointsNorth) provided a version for the annotations in [COCO format](https://cocodataset.org/#format-data). See `coco_annotations` directory. |
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### What data does each instance consist of? |
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Each instance contains a 8-bits RGB image and a text file containing one |
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bounding box description per line. These text files follows the "YOLO |
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format" |
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CLASS CX CY W H |
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*class* is an integer defining the object class – the dataset presents |
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only the grape class that is numbered 0, so every line starts with this |
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“class zero” indicator. The center of the bounding box is the point |
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*(c_x, c_y)*, represented as float values because this format normalizes |
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the coordinates by the image dimensions. To get the absolute position, |
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use *(2048 c_x, 1365 c_y)*. The bounding box dimensions are |
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given by *W* and *H*, also normalized by the image size. |
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The instances presenting mask data for instance segmentation contain |
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files presenting the `.npz` extension. These files are compressed |
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archives for NumPy $n$-dimensional arrays. Each array is a |
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*H X W X n_clusters* three-dimensional array where |
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*n_clusters* is the number of grape clusters observed in the |
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image. After assigning the NumPy array to a variable `M`, the mask for |
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the *i*-th grape cluster can be found in `M[:,:,i]`. The *i*-th mask |
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corresponds to the *i*-th line in the bounding boxes file. |
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The dataset also includes the original image files, presenting the full |
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original resolution. The normalized annotation for bounding boxes allows |
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easy identification of clusters in the original images, but the mask |
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data will need to be properly rescaled if users wish to work on the |
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original full resolution. |
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#### Contributions |
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*For `test_berries.txt` , `train_berries.txt` and `val_berries.txt`*: |
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The berries annotations are following a similar notation with the only |
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exception being that each text file (train/val/test) includes also the |
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instance file name. |
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FILENAME CLASS CX CY |
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where *filename* stands for instance file name, *class* is an integer |
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defining the object class (0 for all instances) and the point *(c_x, c_y)* |
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indicates the absolute position of each "dot" indicating a single berry in |
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a well defined cluster. |
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*For `contrib/berries`*: |
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The annotations provide the *(x, y)* point position for each berry center, in a tabular form: |
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X Y |
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These point-based annotations can be easily loaded using, for example, `numpy.loadtxt`. See `WGISD.ipynb`for examples. |
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[Daniel Angelov (@23pointsNorth)](https://github.com/23pointsNorth) provided a version for the annotations in [COCO format](https://cocodataset.org/#format-data). See `coco_annotations` directory. Also see [COCO format](https://cocodataset.org/#format-data) for the JSON-based format. |
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### Is everything included or does the data rely on external resources? |
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Everything is included in the dataset. |
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### Are there recommended data splits or evaluation measures? |
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The dataset comes with specified train/test splits. The splits are found |
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in lists stored as text files. There are also lists referring only to |
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instances presenting binary masks. |
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| | Images | Boxed clusters | Masked clusters | |
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| ---------------------| -------- | ---------------- | ----------------- | |
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| Training/Validation | 242 | 3,581 | 1,612 | |
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| Test | 58 | 850 | 408 | |
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| Total | 300 | 4,431 | 2,020 | |
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*Dataset recommended split.* |
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Standard measures from the information retrieval and computer vision |
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literature should be employed: precision and recall, *F1-score* and |
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average precision as seen in [COCO](http://cocodataset.org) |
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and [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC). |
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### What experiments were initially run on this dataset? |
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The first experiments run on this dataset are described in [*Grape detection, segmentation and tracking using deep neural networks and three-dimensional association*](https://arxiv.org/abs/1907.11819) by Santos *et al.*. See also the following video demo: |
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[](http://www.youtube.com/watch?v=1Hji3GS4mm4 "Grape detection, segmentation and tracking") |
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**UPDATE**: The JPG files corresponding to the video frames in the [video demo](http://www.youtube.com/watch?v=1Hji3GS4mm4) are now available in the `extras` directory. |
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Data Collection Process |
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----------------------- |
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### How was the data collected? |
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Images were captured at the vineyards of Guaspari Winery, located at |
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Espírito Santo do Pinhal, São Paulo, Brazil (Lat -22.181018, Lon |
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-46.741618). The winery staff performs dual pruning: one for shaping |
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(after previous year harvest) and one for production, resulting in |
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canopies of lower density. The image capturing was realized in April |
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2017 for *Syrah* and in April 2018 for the other varieties. |
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A Canon EOS REBEL T3i DSLR camera and a Motorola Z2 Play smartphone were |
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used to capture the images. The cameras were located between the vines |
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lines, facing the vines at distances around 1-2 meters. The EOS REBEL |
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T3i camera captured 240 images, including all *Syrah* pictures. The Z2 |
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smartphone grabbed 60 images covering all varieties except *Syrah* . The |
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REBEL images were scaled to *2048 X 1365* pixels and the Z2 images |
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to *2048 X 1536* pixels. More data about the capture process can be found |
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in the Exif data found in the original image files, included in the dataset. |
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### Who was involved in the data collection process? |
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T. T. Santos, A. A. Santos and S. Avila captured the images in |
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field. T. T. Santos, L. L. de Souza and S. Avila performed the |
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annotation for bounding boxes and masks. |
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### How was the data associated with each instance acquired? |
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The rectangular bounding boxes identifying the grape clusters were |
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annotated using the [`labelImg` tool](https://github.com/tzutalin/labelImg). |
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The clusters can be under |
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severe occlusion by leaves, trunks or other clusters. Considering the |
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absence of 3-D data and on-site annotation, the clusters locations had |
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to be defined using only a single-view image, so some clusters could be |
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incorrectly delimited. |
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A subset of the bounding boxes was selected for mask annotation, using a |
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novel tool developed by the authors and presented in this work. This |
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interactive tool lets the annotator mark grape and background pixels |
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using scribbles, and a graph matching algorithm developed by [Noma *et al.*](https://doi.org/10.1016/j.patcog.2011.08.017) |
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is employed to perform image segmentation to every pixel in the bounding |
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box, producing a binary mask representing grape/background |
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classification. |
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#### Contributions |
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A subset of the bounding boxes of well-defined (separated and non-occluded |
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clusters) was used for "dot" (berry) annotations of each grape to |
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serve for counting applications as described in [Khoroshevsky *et |
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al.*](https://doi.org/10.1007/978-3-030-65414-6_19). The berries |
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annotation was performed by F. Khoroshevsky and S. Khoroshevsky. |
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Geng Deng ([Deng *et al.*, 2020](https://doi.org/10.1007/978-3-030-63820-7_66)) |
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provided point-based annotations for berries in all 300 images, summing |
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187,374 berries. These annotations are available in `contrib/berries`. |
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Deng *et al.* employed [Huawei ModelArt](https://www.huaweicloud.com/en-us/product/modelarts.html), |
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for their annotation effort. |
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Data Preprocessing |
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------------------ |
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### What preprocessing/cleaning was done? |
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The following steps were taken to process the data: |
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1. Bounding boxes were annotated for each image using the `labelImg` |
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tool. |
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2. Images were resized to *W = 2048* pixels. This resolution proved to |
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be practical to mask annotation, a convenient balance between grape |
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detail and time spent by the graph-based segmentation algorithm. |
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3. A randomly selected subset of images were employed on mask |
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annotation using the interactive tool based on graph matching. |
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4. All binaries masks were inspected, in search of pixels attributed to |
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more than one grape cluster. The annotator assigned the disputed |
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pixels to the most likely cluster. |
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5. The bounding boxes were fitted to the masks, which provided a fine |
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tuning of grape clusters locations. |
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### Was the “raw” data saved in addition to the preprocessed data? |
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The original resolution images, containing the Exif data provided by the |
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cameras, is available in the dataset. |
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Dataset Distribution |
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-------------------- |
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### How is the dataset distributed? |
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The dataset is [available at GitHub](https://github.com/thsant/wgisd). |
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### When will the dataset be released/first distributed? |
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The dataset was released in July, 2019. |
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### What license (if any) is it distributed under? |
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The data is released under [**Creative Commons BY-NC 4.0 (Attribution-NonCommercial 4.0 International license)**](https://creativecommons.org/licenses/by-nc/4.0/). |
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There is a request to cite the corresponding paper if the dataset is used. For |
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commercial use, contact Embrapa Agricultural Informatics business office. |
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### Are there any fees or access/export restrictions? |
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There are no fees or restrictions. For commercial use, contact Embrapa |
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Agricultural Informatics business office. |
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Dataset Maintenance |
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------------------- |
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### Who is supporting/hosting/maintaining the dataset? |
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The dataset is hosted at Embrapa Agricultural Informatics and all |
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comments or requests can be sent to [Thiago T. Santos](https://github.com/thsant) |
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(maintainer). |
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### Will the dataset be updated? |
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There is no scheduled updates. |
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* In May, 2022, [Daniel Angelov (@23pointsNorth)](https://github.com/23pointsNorth) provided a version for the annotations in [COCO format](https://cocodataset.org/#format-data). See `coco_annotations` directory. |
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* In February, 2021, F. Khoroshevsky and S. Khoroshevsky provided the first extension: the berries ("dot") |
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annotations. |
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* In April, 2021, Geng Deng provided point annotations for berries. T. Santos converted Deng's XML files to |
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easier-to-load text files now available in `contrib/berries` directory. |
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In case of further updates, releases will be properly tagged at GitHub. |
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### If others want to extend/augment/build on this dataset, is there a mechanism for them to do so? |
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Contributors should contact the maintainer by e-mail. |
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### No warranty |
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The maintainers and their institutions are *exempt from any liability, |
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judicial or extrajudicial, for any losses or damages arising from the |
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use of the data contained in the image database*. |
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