|
|
| --- |
| license: mit |
| task_categories: |
| - object-detection |
| language: |
| - en |
| pretty_name: Cassava-Dataset (YOLOv8 Format) |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: [] |
| --- |
| |
|
|
| # 🌿 Cassava-Dataset (YOLOv8 Format) |
|
|
| ## 📌 Overview |
|
|
| The **Cassava-Dataset** is a computer vision dataset designed for **plant disease detection in cassava leaves**. |
| It is formatted for **YOLOv8 object detection models**, making it suitable for training deep learning systems for agricultural disease identification. |
|
|
| This dataset was prepared for research and development in **smart agriculture, IoT farming systems, and AI-based plant disease detection**. |
|
|
| --- |
|
|
| ## 🎯 Objective |
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| To enable automated detection and classification of cassava leaf diseases using deep learning models such as: |
|
|
| - YOLOv8 Nano (yolov8n) |
| - YOLOv8 Small/Medium |
| - Other object detection architectures |
|
|
| --- |
|
|
| ## 🧠 Classes |
|
|
| The dataset contains the following classes: |
|
|
| names: |
| - CBB |
| - CBSD |
| - CGM |
| - CMD |
| - HEALTHY |
|
|
| --- |
|
|
| ## 📁 Dataset Structure |
| cassava-7/ |
| ├── train/ |
| │ ├── images/ |
| │ └── labels/ |
| ├── valid/ |
| │ ├── images/ |
| │ └── labels/ |
| ├── test/ |
| │ ├── images/ |
| │ └── labels/ |
| └── data.yaml |
|
|
|
|
| --- |
|
|
| ## ⚙️ YOLOv8 Configuration |
|
|
| Example `data.yaml`: |
|
|
| ```yaml |
| path: . |
| train: train/images |
| val: valid/images |
| test: test/images |
| |
| names: |
| names: |
| - CBB |
| - CBSD |
| - CGM |
| - CMD |
| - HEALTHY |
| ``` |
|
|
| ## 🚀 Usage (YOLOv8 Training) |
| Install dependencies |
| ```yaml |
| pip install ultralytics |
| |
| from ultralytics import YOLO |
| |
| model = YOLO("yolov8n.pt") |
| |
| model.train( |
| data="data.yaml", |
| epochs=50, |
| imgsz=640, |
| batch=16, |
| device=0 |
| ) |
| |
| ``` |
|
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|
|
