Ansem-chaieb/Makerere-Passion-Fruit-Disease-Detection-Challenge

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 …