From b2aabf05a1412d15b5062bb04b136dd4fa60a01b Mon Sep 17 00:00:00 2001
From: xly <ggaq@uestc.edu.cn>
Date: Mon, 9 Sep 2024 18:15:23 +0800
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=201.py?=
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 1.py | 39 ---------------------------------------
 1 file changed, 39 deletions(-)
 delete mode 100644 1.py

diff --git a/1.py b/1.py
deleted file mode 100644
index 60fee3a..0000000
--- a/1.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import torch
-from ultralytics import YOLO
-from ultralytics.data import download
-
-# 下载COCO128数据集
-download('coco128')
-
-# 定义训练参数
-epochs = 10  # 训练轮数
-batch_size = 16  # 批次大小
-img_size = 640  # 输入图像尺寸
-
-# 加载YOLOv8模型
-model = YOLO('yolov8s.yaml')  # 创建新的模型实例
-
-# 开始训练
-model.train(data='coco128.yaml', epochs=epochs, batch=batch_size, imgsz=img_size)
-
-# 加载经过训练的模型，假设模型保存在 'best.pt'
-model = YOLO('best.pt')
-
-# 设置要检测的对象类别，这里的例子是只检测行人
-class_names = model.names
-person_class_id = class_names.index('person')
-
-# 加载图片或视频
-img_path = 'path_to_your_image.jpg'
-
-# 进行目标检测
-results = model(img_path)
-
-# 处理结果
-for result in results:
-    boxes = result.boxes
-    for box in boxes:
-        if box.cls == person_class_id:  # 只处理行人检测结果
-            x1, y1, x2, y2 = box.xyxy[0]  # 获取边界框坐标
-            confidence = box.conf.item()  # 获取置信度
-            print(f"Pedestrian detected at ({x1:.2f}, {y1:.2f}) to ({x2:.2f}, {y2:.2f}), Confidence: {confidence:.2f}")
\ No newline at end of file