[{"createTime":1735734952000,"id":1,"img":"hwy_ms_500_252.jpeg","link":"https://activity.huaweicloud.com/cps.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=V1g3MDY4NTY=&utm_medium=cps&utm_campaign=201905","name":"华为云秒杀","status":9,"txt":"华为云38元秒杀","type":1,"updateTime":1735747411000,"userId":3},{"createTime":1736173885000,"id":2,"img":"txy_480_300.png","link":"https://cloud.tencent.com/act/cps/redirect?redirect=1077&cps_key=edb15096bfff75effaaa8c8bb66138bd&from=console","name":"腾讯云秒杀","status":9,"txt":"腾讯云限量秒杀","type":1,"updateTime":1736173885000,"userId":3},{"createTime":1736177492000,"id":3,"img":"aly_251_140.png","link":"https://www.aliyun.com/minisite/goods?userCode=pwp8kmv3","memo":"","name":"阿里云","status":9,"txt":"阿里云2折起","type":1,"updateTime":1736177492000,"userId":3},{"createTime":1735660800000,"id":4,"img":"vultr_560_300.png","link":"https://www.vultr.com/?ref=9603742-8H","name":"Vultr","status":9,"txt":"Vultr送$100","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":5,"img":"jdy_663_320.jpg","link":"https://3.cn/2ay1-e5t","name":"京东云","status":9,"txt":"京东云特惠专区","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":6,"img":"new_ads.png","link":"https://www.iodraw.com/ads","name":"发布广告","status":9,"txt":"发布广告","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":7,"img":"yun_910_50.png","link":"https://activity.huaweicloud.com/discount_area_v5/index.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=aXhpYW95YW5nOA===&utm_medium=cps&utm_campaign=201905","name":"底部","status":9,"txt":"高性能云服务器2折起","type":2,"updateTime":1735660800000,"userId":3}]
目标检测在许多行业中都有许多实际应用。大多数时候，在工业环境中，物体检测目标很小。因此，有效地训练目标检测模型变得非常困难。其中一个问题是
钢材表面缺陷检测。即使使用深度学习，也很难高精度地解决问题。在本文中将详细讲述基于 YOLO-NAS 实现钢铁表面的缺陷检测。
第一步：安装
注意：安装完成后（可能需要几分钟），您需要在安装完成后重新启动运行时。
pip install super-gradients==3.1.0
这是一个“一体化”的深度学习训练库，用于计算机视觉模型
第二步：导入库
import os from pathlib import Path from typing import Dict, Union import
xml.etree.ElementTree as ET import requests import torch from PIL import Image
from super_gradients.training import Trainer, dataloaders, models from
super_gradients.training.dataloaders.dataloaders import (
coco_detection_yolo_format_train, coco_detection_yolo_format_val ) from
super_gradients.training.losses import PPYoloELoss from
super_gradients.training.metrics import DetectionMetrics_050 from super_
