116 - Person类

本文详细介绍了一个包含姓名、性别和年龄属性的Person类的构造过程。提供了set和get方法用于属性的设置与获取,并实现了print方法用于打印用户信息。示例输入输出展示了如何使用该类。

116 - Person类

Time Limit: 1000   Memory Limit: 65535
Submit: 990  Solved: 362

Description

构造Person类。包括姓名(name),性别(sex)和年龄(age)。提供所有属性的set和get函数,提供print函数打印其信息

Input

姓名(name),性别(sex)和年龄(age)

Output

用户信息

Sample Input

Lucy male 23

Sample Output

name:Lucy; sex:male; age:23
class Person
{
	private String name;
	private String sex;
	private int age;
	public Person()
	{
		name = null;
		sex = null;
		age = 0;
	}
	public void setName(String name)
	{
		this.name = name;
	}
	public void setSex(String sex)
	{
		this.sex = sex;
	}
	public void setAge(int age)
	{
		this.age = age;
	}
	public void print()
	{
		System.out.print("name:"+name+"; sex:"+sex+"; age:"+age);
	}
	
}

 

(yolov8) F:\yolov8>yolo detect val data=widerperson.yaml model=yolov8n.pt Ultralytics 8.3.129 Python-3.9.21 torch-2.6.0+cu118 CUDA:0 (NVIDIA GeForce RTX 3050 Laptop GPU, 4096MiB) YOLOv8n summary (fused): 72 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPs val: Fast image access (ping: 0.00.0 ms, read: 103.2210.6 MB/s, size: 55.1 KB) val: Scanning F:\yolov8\datasets\widerperson\val\labels... 22 images, 0 backgrounds, 22 corrupt: 100%|██████████| 22/22 [00:00<00:00, 814.59it/s] val: F:\yolov8\datasets\widerperson\val\images\000040.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000041.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000042.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000044.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000045.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000046.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000048.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000049.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000050.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000051.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000059.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000060.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000061.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000065.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000079.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000080.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000081.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000085.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000088.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000089.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000091.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: F:\yolov8\datasets\widerperson\val\images\000099.jpg: ignoring corrupt image/label: Label class 1 exceeds dataset class count 1. Possible class labels are 0-0 val: New cache created: F:\yolov8\datasets\widerperson\val\labels.cache WARNING No images found in F:\yolov8\datasets\widerperson\val\labels.cache, training may not work correctly. See https://docs.ultralytics.com/datasets for dataset formatting guidance. Traceback (most recent call last): File "F:\anaconda\envs\yolov8\lib\runpy.py", line 197, in _run_module_as_main return _run_code(code, main_globals, None, File "F:\anaconda\envs\yolov8\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "F:\anaconda\envs\yolov8\Scripts\yolo.exe\__main__.py", line 7, in <module> File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\cfg\__init__.py", line 981, in entrypoint getattr(model, mode)(**overrides) # default args from model File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\engine\model.py", line 630, in val validator(model=self.model) File "F:\anaconda\envs\yolov8\lib\site-packages\torch\utils\_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\engine\validator.py", line 190, in __call__ self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch) File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\models\yolo\detect\val.py", line 317, in get_dataloader dataset = self.build_dataset(dataset_path, batch=batch_size, mode="val") File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\models\yolo\detect\val.py", line 304, in build_dataset return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride) File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\data\build.py", line 109, in build_yolo_dataset return dataset( File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\data\dataset.py", line 87, in __init__ super().__init__(*args, channels=self.data["channels"], **kwargs) File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\data\base.py", line 113, in __init__ self.labels = self.get_labels() File "F:\anaconda\envs\yolov8\lib\site-packages\ultralytics\data\dataset.py", line 192, in get_labels len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths)) ValueError: not enough values to unpack (expected 3, got 0) 使用yolov8官方验证工具给出的结果,分析一下问题所在
最新发布
05-14
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