plant-pathology-2021
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## Description Dataset from the Plant Pathology 2021 (FGVC8) Challenge. ' For Plant Pathology 2021-FGVC8, we have significantly increased the number of foliar disease images and added additional disease categories. This year’s dataset contains approximately 23,000 high-quality RGB images of apple foliar diseases, including a large expert-annotated disease dataset. This dataset reflects real field scenarios by representing non-homogeneous backgrounds of leaf images taken at different maturity stages and at different times of day under different focal camera settings. ' The original dataset has one train split and a test split that was hidden for the challenge. I have taken 10% of train for a validation, using stratified sampling. I do not have access to the test samples. - Website: - https://www.kaggle.com/c/plant-pathology-2021-fgvc8 - https://sites.google.com/view/fgvc8/competitions/plantpathologychallenge2021 ## Usage This dataset is serving as a canonical example for multi-label image classificatino datasets with `timm`. The additions to train & val scripts for this are a WIP... ## Citation ``` Thapa, Ranjita, Zhang, Kai, Snavely, Noah, Belongie, Serge, and Khan, Awais. Plant Pathology 2021 - FGVC8. https://kaggle.com/competitions/plant-pathology-2021-fgvc8, 2021. Kaggle. ```
## 数据集描述 本数据集源自植物病理学2021（FGVC8）挑战赛。 针对植物病理学2021-FGVC8赛事，我们大幅扩充了叶部病害图像的数量，并新增了病害类别。本年度数据集包含约23000张高质量苹果叶部病害RGB图像，其中包含一个规模可观的专家标注病害数据集。该数据集通过呈现不同成熟阶段、不同拍摄时段、不同相机对焦设置下的叶片图像非均匀背景，还原了真实的田间场景。 原始数据集包含一个训练划分集与一个赛事保密的测试划分集。本次处理中，我采用分层抽样方法从训练集中抽取10%作为验证集。我无法获取测试样本。 - 相关网址： - Kaggle赛事页面：https://www.kaggle.com/c/plant-pathology-2021-fgvc8 - FGVC8官方赛事页面：https://sites.google.com/view/fgvc8/competitions/plantpathologychallenge2021 ## 使用场景 本数据集作为`timm`框架下多标签图像分类数据集的标准范例。针对该数据集的训练与验证脚本扩充工作仍在进行中（WIP）。 ## 引用格式 Thapa, Ranjita, Zhang, Kai, Snavely, Noah, Belongie, Serge, and Khan, Awais. 植物病理学2021 - FGVC8. https://kaggle.com/competitions/plant-pathology-2021-fgvc8, 2021. Kaggle.
