上海娱乐场所关门亚马逊关键词快速优化

有时候我们可能只需要用yolo模型检测个别类别，并显示，这就需要知道id，以下代码可打印出

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from ultralytics import YOLO# 加载模型
model = YOLO('yolo11x.pt')# 打印所有类别名称及其对应的ID
print(model.names)

{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}

例如桔子的id是49

from ultralytics import YOLO
import cv2# 加载模型
model = YOLO('yolo11x.pt')# 运行预测，仅检测桔子（假设桔子的类别ID为49，请根据实际模型调整）
results = model.predict("d.jpg", imgsz=640, save=False, device=0, classes=[49])# 获取第一个结果对象
result = results[0]# 获取检测结果图像（自动过滤非指定类别）
result_image = result.plot()# 判断是否检测到桔子
if len(result.boxes) == 0:print("no orange")# 可以在图像上添加文字提示cv2.putText(result_image, "no orange", (50, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 3)
else:print(f"Detected {len(result.boxes)} oranges")# 显示和保存结果
cv2.imshow('Detection Result', result_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('dec.jpg', result_image)

 yolo11x.pt比yolo11n.pt 检测慢，但要更精确