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Pada artikel ini dibahas usaha untuk mereplikasi solusi dari GWC_solution

Hardware yang dipakai: RTX 3090

Catatan

• RTX3090 tidak kompatibel dengan Cuda 10.2 , minimal perlu CUDA 11.3
• PyTorch 1.9.0 tidak kompatibel dengan CUDA 11.3, sehingga perlu diganti dengan PyTorch 1.10.0
• Script GWC_YOLOv5 tidak kompatibel dengan PyTorch 1.12.0

Prosedur

Create Conda environment

conda create --name gwc8 python=3.7.10 scipy=1.4.
conda activate gwc8

Install PyTorch

pip3 install torch==1.10.0 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113

Install prerequisites

pip install -r requirements.txt
pip install numpy==1.19.5
pip uninstall -y PyYAML
pip install PyYAML==5.3.1
pip install ensemble_boxes
pip install setuptools==59.5.0
pip install jupyter
pip install matplotlib
pip install opencv-python
pip install tensorboard

Clone GWC_solution

cd /home/admin
git clone https://github.com/ksnxr/GWC_solution.git

Clone GWC_YOLOv5

cd GWC_solution
git clone https://github.com/ksnxr/GWC_YOLOv5.git

Jalankan jupyter notebook

jupyter notebook --allow-root --no-browser --ip=0.0.0.0

Setelah itu buka jupyter notebook dari browser

Eksekusi script berikut

• Use data-cleaning.ipynb to generate clean_train.csv.
• Use KFold.ipynb to generate 4-folds train and validation indexes.
• Use basis-0.ipynb, basis-1.ipynb.basis-2.ipynb and basis-3.ipynb to train on the four folds.
• Use pseudo-original-0.ipynb, pseudo-original-1.ipynb, pseudo-original-2.ipynb, pseudo-original-3.ipynb and pseudo-master-3.ipynb to train 5 pseudo models.
• Use get-labels.ipynb to ensemble the 5 weights obtained in step 3 and generate labels for subsequent training.
• Use pseudo-master-final.ipynb to train the final model.

Training

python train.py --name 4fold0 --img 800 --batch 8 --epochs 35 --data custom.yaml --weights yolov5x.pt --cache-images --save_period 1

Monitor training

cd /home/admin/GWC_solution/GWC_YOLOv5
tensorboard --logdir runs/train  --bind_all

Berikut ini data flow diagram dari proses komputasi

Referensi

• GWC Solution
• GWC YOLOv5
• AICrowd Competition
• Global Wheat Dataset
• Global Wheat Head Dataset 2021 relased
• High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model https://www.frontiersin.org/articles/10.3389/fpls.2022.787852/full