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围栏破损检测数据集的训练及应用

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# 1. 安装依赖!pip install-q roboflow ultralytics project=rf.workspace("iveia").project("broken-fence")dataset=project.version(12).download(model_format="yolov8",location="./broken-fence-yolov8",overwrite=True)print("Dataset path:",dataset.location)

# 3. 使用 YOLOv8 训练目标检测模型fromultralyticsimportYOLO# 使用 YOLOv8 nano 预训练权重，适合快速实验model=YOLO("yolov8n.pt")results=model.train(data=f"{dataset.location}/data.yaml",epochs=100,imgsz=640,batch=16,device=0,# 有 GPU 用 0；CPU 可改成 "cpu"workers=4,project="runs/train",name="broken_fence_yolov8n",plots=True)

# 4. 验证模型fromultralyticsimportYOLO best_model=YOLO("runs/train/broken_fence_yolov8n/weights/best.pt")metrics=best_model.val()print(metrics)

# 5. 推理测试fromultralyticsimportYOLO model=YOLO("runs/train/broken_fence_yolov8n/weights/best.pt")results=model.predict(source="./broken-fence-yolov8/test/images",conf=0.25,save=True)

# 6. 可选：导出模型model.export(format="onnx")

如果是写论文或实验报告，可以把模型改成yolov8s.pt或yolov8m.pt做对比实验。