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feat: YOLO_Detection_train 3type_box_detection #22
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from ultralytics import YOLO | ||
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# Load a COCO-pretrained YOLO11n model | ||
model = YOLO("yolo11x.pt") | ||
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# Train the model on the COCO8 example dataset for 100 epochs | ||
results = model.train( | ||
data="/data/ephemeral/home/MCG/yolo_dataset_split/data.yaml", | ||
epochs=500, | ||
imgsz=2048, | ||
batch=2, | ||
hsv_h=0.0, # Hue shift 비활성화 | ||
hsv_s=0.0, # Saturation shift 비활성화 | ||
hsv_v=0.2, # Brightness shift 비활성화 | ||
degrees=0.2, # 이미지 회전 비활성화 | ||
translate=0.0, # 이미지 이동 비활성화 | ||
scale=0.2, | ||
shear=0.2, # 이미지 비틀기 비활성화 | ||
perspective=0.0, # 원근 변환 비활성화 | ||
flipud=0.0, # 상하 뒤집기 비활성화 | ||
mosaic=0.0, # Mosaic 비활성화 | ||
mixup=0.0, # Mixup 비활성화 | ||
copy_paste=0.0 , # Copy-Paste 비활성화 | ||
erasing=0.0, | ||
crop_fraction=0.0 | ||
) | ||
#유지한 증강: Scale, degrees, shear, hsv_v, fliplr |