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Data Setting for YOLOv5 
Parent/datasets/MakeData$ python saveData.py
100%|████████████████████████████████████████████████████████████████████████| 2235/2235 [00:45<00:00, 49.47it/s]
==============================
No. Total Data:  2235
==============================
Training Data: No. Images 1861
Training Data: No. GT 1861
Validation Data: No. Images 187
Validation Data: No. GT 187
Test Data: No. Images 187
Test Data: No. GT 187
==============================
No. Total Image Data:  2235
No. Total GT Data:  2235
==============================
Parent/datasets/MakeData$ python labelme2YOLOv5.py
100%|█████████████████████████████████████████████| 5/5 [00:00<00:00,  9.03it/s]
==============================
No. Total Data:  5
==============================
Training Data: No. Images 3
Training Data: No. GT 3
Validation Data: No. Images 2
Validation Data: No. GT 2
==============================
No. Total Image Data:  5
No. Total GT Data:  5
==============================

Train

FlickrLogos_47

Parent/YOLOv5$ python segment/train.py --data LogoRec.yaml --epochs ${epoch} --batch-size ${batch_size} --weights yolov5${모델 버전}-seg.pt #--resume

labelme

Parent/YOLOv5$ python segment/train.py --data labelme.yaml --epochs ${epoch} --batch-size ${batch_size} --weights ${weights}.pt
  • --data: 데이터의 정보가 저장된 .yaml 파일 지정
  • --epochs: Training 시 사용될 epoch의 수 지정
  • --batch-size: Training 시 사용될 batch size 지정
  • --weights: Fine-tuning에 사용될 pre-trained 가중치
  • --resume: Training을 이어서 할 수 있는 옵션

Validation

Parent/YOLOv5$ python segment/val.py --data LogoRec.yaml --batch-size ${batch_size} --weights ${weights}
  • --data: 데이터의 정보가 저장된 .yaml 파일 지정
  • --batch-size: Validation 시 사용될 batch size 지정
  • --weights: Validation을 위해 사용할 가중치

Test

Detection

Parent/YOLOv5$ python segment/predict.py --weights runs/train-seg/${훈련된 가중치}/weights/best.pt --source ../datasets/LogoRec/images/test --conf-thres ${threshold} --bms 0

Segmentation

Parent/YOLOv5$ python segment/predict.py --weights runs/train-seg/${훈련된 가중치}/weights/best.pt --source ../datasets/LogoRec/images/test --conf-thres ${threshold} --bms 1

Mosaic

Parent/YOLOv5$ python segment/predict.py --weights runs/train-seg/${훈련된 가중치}/weights/best.pt --source ../datasets/LogoRec/images/test --conf-thres ${threshold} --bms 2

🎉 Demo! 🎉

Parent/YOLOv5$ python segment/predict.py --weights runs/train-seg/${훈련된 가중치}/weights/best.pt --source 0 --conf-thres ${threshold} --bms 3
  • --weights: Test를 위해 사용할 가중치
  • --source: Test를 위해 사용할 데이터 (0으로 지정 시 캠 사용)
  • --conf-thres: Confidence threshold
  • --bms: BoonMoSa! (For Real-Time Operation)
    • 0: Detection
    • 1: Segmentation
    • 2: Mosaic
    • 3: Demo (Raw Image -> Detection -> Segmentation -> Mosaic)