Note

This repository includes the official implementation of the paper:

Data-centric Annotation Analysis for Plant Disease Detection: Strategy, Consistency, and Performance.

Authors and affiliations:

Jiuqing Dong 1, Jaehwan Lee1, 2, Alvaro Fuentes 1,2, Sook Yoon 3,, Mun Haeng Lee 4, Dong Sun Park 1,2, 1 Department of Electronic Engineering, Jeonbuk National University, Jeonju, South Korea 2 Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, South Korea 3 Department of Computer Engineering, Mokpo National University, Muan, South Korea 4 Fruit Vegetable Research Institute, Chungnam A.R.E.S, Buyeo, South Korea

The code include YOLO-v5 implement, Noise generation, Data-augmentation by rotation, Visualization module, and Label processing part. In addition, we will provide the pretrained model of our paprika dataset. However, due to non-disclosure agreements, we are temporarily unable to make the dataset public.

You also can refer to the Official implement of YOLO-v5. https://github.com/ultralytics/yolov5

You need to modify the path to be able to run on your dataset.

Install

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

cd yolov5

pip install -r requirements.txt # install

Training

python train.py --img 640 --batch 16 --epochs 200 --data data/paprika_v4.yaml --cfg models/yolov5x.yaml --weights weights/yolov5x.pt --device 1 --project paprika_v4/x

--data: the configuration of training.

-- weights: the pre-trained model

--divice: GPU index

--project: a folder for saving output

Testing

python test.py --data data/paprika_v4.yaml --weights paprika_v4/x/exp/weights/best.pt --device 2 --project paprika_v4/x

Inference

python detect.py --source /home/multiai3/Jiuqing/yolo5-official-new/datasets/paprika_v4/images/test --weights paprika_v4/x/exp/weights/best.pt --device 2 --project paprika_v4/x

--source: source of image.

Visualization

python detect_visualization.py --source /home/multiai3/Jiuqing/yolo5-official-new/datasets/paprika_v4/images/test --weights paprika_v4/x/exp/weights/best.pt --device 2 --project paprika_v4/x

--source: source of image.

Data augmentation

Please refer to ./tools/process_images/data_aug_*.py

Split the datasets

Please refer to ./tools/process_images/split_the_datasets.py

Modify orientatiion

Please refer to https://medium.com/@ageitgey/the-dumb-reason-your-fancy-computer-vision-app-isnt-working-exif-orientation-73166c7d39da

If you don't have this problem, please ignore this.

Otherwise, please refer to ./tools/process_images/modify_orientation.py

Noise generation:

Please refer to ./tools/process_labels/noise*.py

Calculate the instance

Please refer to ./tools/process_labels/calculate_bounding_box.py

Show your annotations

Please refer to ./tools/show_xml/*.py

Pre-trained model

The pretrained model will be released as soon as possible.