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---
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
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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