pistols.zip
pistols
 - export
  + images
    [사진]
  + labels
    [좌표값]
 data.yaml



-----------------------------------
!unzip -q ../pistols.zip -d ../pistols
-----------------------------------

from glob import glob
img_list = glob('/content/pistols/export/images/*.jpg')
print(len(img_list))

-------------------------------------

from sklearn.model_selection import train_test_split
train_img_list, val_img_list = train_test_split(img_list, test_size=0.2, random_state=2000)
print(len(train_img_list), len(val_img_list))

----------------------------
with open('/content/pistols/export/train.txt', 'w') as f:
  f.write('\n'.join(train_img_list) + '\n')

with open('/content/pistols/export/val.txt', 'w') as f:
  f.write('\n'.join(val_img_list) + '\n')
----------------------------------------

import yaml

with open('/content/pistols/data.yaml', 'r') as f:
  data = yaml.safe_load(f)

print(data)

data['train'] = '/content/pistols/export/train.txt'
data['val'] = '/content/pistols/export/val.txt'

with open('/content/pistols/data.yaml', 'w') as f:
  yaml.dump(data, f)

print(data)

-----------------------------------------------
!python train.py --img 416 --batch 16 --epochs 3 --data /content/pistols/data.yaml --cfg ./models/yolov5s.yaml --weights yolov5s.pt --nosave --cache --name pistols

--img : 이미지 크기
--batch : 배치 크기
--epochs : epoch 크기
--data : 데이터파일 (data.yaml 파일 경로 지정)
--cfg : 위에서 정한 모델 크기 (yolov5/models 폴더에 yaml파일로 저장되어 있음)
--weights : 미리 학습된 모델로 학습할 경우 (yolov5s.pt 등의 형식으로 다운로드 가능)
--name : 학습된 모델의 이름


!python detect.py --weights runs/train/exp22/weights/last.pt --img 640 --conf 0.25 --source data/images/1.jpg



#https://public.roboflow.com/
# https://www.makesense.ai/
#https://github.com/ultralytics/yolov5