| --- |
| 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. |
|
|
