Manya20/CADI-AI

--- license: cc-by-sa-4.0 task_categories: - object-detection language: - en tags: - object detection - vision size_categories: - 1K<n<10K extra_gated_heading: "Acknowledge license to accept the repository" extra_gated_button_content: "Acknowledge license" extra_gated_fields: I agree to attribute the creator of this repository: checkbox --- --- ## Cashew Disease Identication with Artificial Intelligence (CADI-AI) Dataset This repository contains a comprehensive dataset of cashew images captured by drones, accompanied by meticulously annotated labels. Each high-resolution image in the dataset has a resolution of 1600x1300 pixels, providing fine details for analysis and model training. To facilitate efficient object detection, each image is paired with a corresponding text file in YOLO format. The YOLO format file contains annotations, including class labels and bounding box coordinates. ### Dataset Labels ``` ['abiotic', 'insect', 'disease'] ``` ### Number of Images ```json {'train': 3788, 'valid': 710, 'test': 238} ``` ### Number of Instances Annotated ```json {'insect':1618, 'abiotic':13960, 'disease':7032} ``` ### Folder structure after unzipping repective folders ```markdown Data/ └── train/ ├── images ├── labels └── val/ ├── images ├── labels └── test/ ├── images ├── labels ``` ### Dataset Information The dataset was created by a team of data scientists from the KaraAgro AI Foundation, with support from agricultural scientists and officers. The creation of this dataset was made possible through funding of the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) through their projects [Market-Oriented Value Chains for Jobs & Growth in the ECOWAS Region (MOVE)](https://www.giz.de/en/worldwide/108524.html) and [FAIR Forward - Artificial Intelligence for All](https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/), which GIZ implements on behalf the German Federal Ministry for Economic Cooperation and Development (BMZ). For detailed information regarding the dataset, we invite you to explore the accompanying datasheet available [here](https://drive.google.com/file/d/1viv-PtZC_j9S_K1mPl4R1lFRKxoFlR_M/view?usp=sharing). This comprehensive resource offers a deeper understanding of the dataset's composition, variables, data collection methodologies, and other relevant details.协议：CC BY-SA 4.0（知识共享署名-相同方式共享4.0） 任务类别：目标检测 语言：英语 标签：目标检测、视觉 样本量范围：1000 < 样本量 < 10000 申请访问须知标题：确认许可协议以接收该数据集仓库 申请按钮文本：确认许可协议 申请访问字段：我同意为本仓库的创建者署名：复选框 --- ## 基于人工智能的腰果病害识别（CADI-AI）数据集 本仓库收录了一套由无人机采集的腰果图像数据集，所有图像均附带经过精细标注的标签。数据集中的每张高分辨率图像分辨率均为1600×1300像素，可提供充足的细节用于分析与模型训练。为适配高效目标检测任务，每张图像均配套一个YOLO格式的对应文本文件，该文件包含标注信息，涵盖类别标签与边界框坐标。 ### 数据集标签 ['非生物胁迫', '虫害', '病害'] ### 图像数量 json {'训练集': 3788, '验证集': 710, '测试集': 238} ### 标注实例总数 json {'虫害': 1618, '非生物胁迫': 13960, '病害': 7032} ### 解压后的目录结构 markdown Data/ └── train/ ├── images ├── labels └── val/ ├── images ├── labels └── test/ ├── images ├── labels ### 数据集详情 本数据集由KaraAgro AI基金会的数据分析团队创建，并得到农业科学家与农技人员的支持。本数据集的制作得到了德国国际合作机构（GIZ）的项目资助，该机构代表德国联邦经济合作与发展部（BMZ）实施了两项项目：分别是《西非国家经济共同体区域面向市场的价值链促进就业与增长项目（MOVE）》（链接：https://www.giz.de/en/worldwide/108524.html）以及《FAIR Forward——全民人工智能项目》（链接：https://www.bmz-digital.global/en/overview-of-initiatives/fair-forward/）。 如需了解本数据集的详细信息，敬请查阅随附的数据手册：[点击此处](https://drive.google.com/file/d/1viv-PtZC_j9S_K1mPl4R1lFRKxoFlR_M/view?usp=sharing)。该手册内容全面，可帮助您深入了解数据集的构成、变量设置、数据采集方法及其他相关细节。