아주 잘 설명되어 있음!
usage: train.py [-h] [--weights WEIGHTS] [--cgf CFG] [--data DATA] [--hyp HYP] [--epochs EPOCHS] [--batch-size BATCH_SIZE] [--img-size IMG_SIZE [IMG_SIZE ...]] [-rect] [-resume [RESUME]] [-nosave] [notest] [--noautoanchor] [--evolve] [--bucket BUCKET] [--cache-images] [--image-weights] [--name NAME] [--device DEVICES] [--multi-scale] [--single-cls] [--adam] [--sync-bn] [--local_rank LOCAL_RANK] [--logdir LOGDIR] [--log-imgs LOG_IMGS] [--workers WORKERS]
bigdata-analyst.tistory.com/195
yolov5 github
Tutorials : Train Custom Data
github.com/ultralytics/yolov5/wiki/Train-Custom-Data
-> 구글 코랩
dataset/https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb
blog.roboflow.com/how-to-train-yolov5-on-a-custom-
YOLOv5 Tutorial
medium.com/@michaelohanu/yolov5-tutorial-75207a19a3aa
YOLOv5 예제 (Mask)
public.roboflow.com/object-detection/mask-wearing
Darknet
pjreddie.com/darknet/yolo/#demo
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