Ansem-chaieb/Makerere-Passion-Fruit-Disease-Detection-Challenge
Domain:
agricultureRecord type:
datasetproject
Creator:
Ans
Host:
Passion fruit pests and diseases in Uganda lead to reduced yields and decreased investment in farming over time. Most Ugandan farmers (including passion fruit farmers) are smallholder farmers from low-income households and do not have sufficient information and means to combat these challenges.
# Grenadilla Disease Detection Challenge
The objective of this challenge is to classify the disease status of a plant
given an image of a passion fruit. You need to classify each fruit individually
and not assume that all the fruit in the same image have the same status.
## Input dataset
The dataset contains about 4000 images resized to **512x512**. There are **~5000** fruit in total.
Some images contain more than one fruit and thus more than one bounding box.
The images are annotated using bounding boxes defined in a **COCO format** and each bounding box is
tagged to one of three classes:
* **Fruit healthy**.
* **Fruit brownspot**.
* **fruit woodiness**.
## Instructions
**1 . Clone the repository:**
```bash
git clone github.com
cd Makerere-Passion-Fruit-Disease-Detection-Challenge
```
**2 . Download custom YOLOv5 object detection data:**
```bash
zindi_dataset/
└── Test_Images/
└── Train_Images/
└── Test.csv
└── Train.csv
└── Sample_submission.csv
```
**3 . Clone YOLOv5 repository:**
```bash
git clone github.com
```
Install YOLOv5 dependencies:
```bash
pip install -U -r yolov5/requirements.txt
```
**4 . Set configuration:**
Data Configuration:
- Set data paths, image size, id, target and bbox columns.
- Create custom data yaml file.
```bash
yolov5/
└── data/
└── makerere.yaml
```
```bash
train: yolo_dataset/makerere/images/train
val : yolo_dataset/makerere/images/validation
nc : 3
names : [ 'fruit_brownspot', 'fruit_healthy', 'fruit_woodiness']
```
Define YOLOv5 Model Configuration and Architecture:
- Set batch size, number of epochs and weights.
#### Run project
```bash
python3 main.py --information --display --process_data --train --inference
```
The command comes with 5 flags:
**--information:** Get informtions about your dataset.
**--display:** Plot batch of dataset images.
**--process_data:** Process coordinates from pascal voc to …