30 lines
1.5 KiB
Python
30 lines
1.5 KiB
Python
"""
|
|
rbs_train
|
|
Общая задача: web-service pipeline
|
|
Реализуемая функция: обучение нейросетевой модели по заданному BOP-датасету
|
|
|
|
python3 $PYTHON_EDUCATION --path /Users/idontsudo/webservice/server/build/public/7065d6b6-c8a3-48c5-9679-bb8f3a690296 \
|
|
--name test1234 --datasetName 32123213
|
|
|
|
27.04.2024 @shalenikol release 0.1
|
|
"""
|
|
|
|
import argparse
|
|
from train_Yolo import train_YoloV8
|
|
from train_Dope import train_Dope_i
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--path", required=True, help="Path for dataset")
|
|
parser.add_argument("--name", required=True, help="String with result weights name")
|
|
parser.add_argument("--datasetName", required=True, help="String with dataset name")
|
|
parser.add_argument("--outpath", default="weights", help="Output path for weights")
|
|
parser.add_argument("--type", default="ObjectDetection", help="Type of implementation")
|
|
parser.add_argument("--epoch", default=3, type=int, help="How many training epochs")
|
|
parser.add_argument('--pretrain', action="store_true", help="Use pretraining")
|
|
args = parser.parse_args()
|
|
|
|
if args.type == "ObjectDetection":
|
|
train_YoloV8(args.path, args.name, args.datasetName, args.outpath, args.epoch, args.pretrain)
|
|
else:
|
|
train_Dope_i(args.path, args.name, args.datasetName, args.outpath, args.epoch, args.pretrain)
|