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Embrapa WGISD
许可协议: CC-BY-NC

Overview

Embrapa WGISD (Wine Grape Instance Segmentation Dataset) was created to provide images and annotation to study object detection and instance segmentation for image-based monitoring and field robotics in viticulture. It provides instances from five different grape varieties taken on field. These instances shows variance in grape pose, illumination and focus, including genetic and phenological variations such as shape, color and compactness.
Possible uses include relaxations of the instance segmentation problem: classification (Is a grape in the image?), semantic segmentation (What are the "grape pixels" in the image?), and object detection (Where are the grapes in the image?). The WGISD can also be used in grape variety identification.
The building of the WGISD dataset was supported by the Embrapa SEG Project 01.14.09.001.05.04, Image-based metrology for Precision Agriculture and Phenotyping, and the CNPq PIBIC Program (grants 161165/2017-6 and 125044/2018-6).

数据概要
数据格式
image,
数据量
--
文件大小
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发布方
Thiago T. Santos
| 数据量 -- | 大小 --
Embrapa WGISD
许可协议: CC-BY-NC

Overview

Embrapa WGISD (Wine Grape Instance Segmentation Dataset) was created to provide images and annotation to study object detection and instance segmentation for image-based monitoring and field robotics in viticulture. It provides instances from five different grape varieties taken on field. These instances shows variance in grape pose, illumination and focus, including genetic and phenological variations such as shape, color and compactness.
Possible uses include relaxations of the instance segmentation problem: classification (Is a grape in the image?), semantic segmentation (What are the "grape pixels" in the image?), and object detection (Where are the grapes in the image?). The WGISD can also be used in grape variety identification.
The building of the WGISD dataset was supported by the Embrapa SEG Project 01.14.09.001.05.04, Image-based metrology for Precision Agriculture and Phenotyping, and the CNPq PIBIC Program (grants 161165/2017-6 and 125044/2018-6).

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