21 lines
1007 B
Python
21 lines
1007 B
Python
from ultralytics import YOLO
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# from ultralytics.yolo.utils.benchmarks import benchmark
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# Load a model
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# model = YOLO("yolov8n.yaml") # build a new model from scratch
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# model = YOLO("../runs/detect/train3/weights/best.pt") # zhou
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model = YOLO("../runs/detect/train4/weights/best.pt") # 1000 img, mine
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model = YOLO("../runs/detect/train2/weights/best.pt") # 1000 img, based on 3000 img
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# model = YOLO("../../../project/runs/detect/train3/weights/best.pt") # 3000 img, mine
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# Use the model
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# model.train(data="coco128.yaml", epochs=3,workers=0) # train the model,workers=0 if windows
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# metrics = model.val() # evaluate model performance on the validation set
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img_path = "./image"
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results = model.predict(img_path, save = True) # device=0 by default, conf:置信度阈值
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# results = model.predict(img_path,save=True,classes=[0,2],conf=0.5) # i.e. classes=0,classes=[0,3,4]
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# save detection results *
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# results = model.predict(img_path,save=True,save_txt=True,classes=0,conf=0.4)
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