Yolov5 accuracy test

yolov5_val.sh

#docker run --gpus=all -it -v /:/host/ nvcr.io/nvidia/pytorch:22.02-py3
host_folder="/host/tmp/yolov5_results.txt"
rm ${host_folder}

git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -qr requirements.txt
rm requirements.txt
pip uninstall opencv-python
pip install opencv-python-headless==4.4.0.40 ujson
#wget https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip
#unzip -q coco2017val.zip -d ../ && rm coco2017val.zip

# apt update ; apt-get install libfreetype6-dev -y ; pip install -U pillow

yolov5_variants=("yolov5n" "yolov5s" "yolov5m" "yolov5l" "yolov5x")
image_sizes=("320" "384" "416" "512" "608" "640")

for variant in "${yolov5_variants[@]}"; do
  for size in "${image_sizes[@]}"; do
    echo -e "------ ${variant} ${size} ------" >> ${host_folder} 2>&1 
    python val.py --weights ${variant}.pt --data coco.yaml --batch-size 8 --img ${size} --conf 0.001 --iou 0.65 --save-txt --verbose  2>&1 | tee -a ${host_folder}  # mAP
  done
done