基于mindspore的口罩检测训练与在线推理

MindSpore在昇腾设备上训练YOLOv5口罩检测教程

最新推荐文章于 2025-08-04 19:56:59 发布

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最新推荐文章于 2025-08-04 19:56:59 发布 · 5.2k 阅读

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#深度学习 #目标检测 #人工智能

本文介绍了如何在昇腾Atlas800服务器上使用MindSpore安装和配置YOLov5模型，处理VOC MASK数据集，并详细讲解了数据预处理、模型训练及在线推理的过程。

mindspore安装地址：https://www.mindspore.cn/install

mindspore开源模型库：https://gitee.com/mindspore/models

测试平台为昇腾Atlas800训练服务器，Ubuntu18.04系统，搭载昇腾910AI加速芯片，FP16算力为320T。

数据集为VOC_MASK口罩数据集，在使用数据集之前需要对数据集文件进行转换，将voc标注文件由xml转为coco的json格式。

转换脚本：

import os
import glob
import json
import shutil
import numpy as np
import xml.etree.ElementTree as ET
 
 
 
START_BOUNDING_BOX_ID = 1
 
def get(root, name):
    return root.findall(name)
 
 
def get_and_check(root, name, length):
    vars = root.findall(name)
    if len(vars) == 0:
        raise NotImplementedError('Can not find %s in %s.'%(name, root.tag))
    if length > 0 and len(vars) != length:
        raise NotImplementedError('The size of %s is supposed to be %d, but is %d.'%(name, length, len(vars)))
    if length == 1:
        vars = vars[0]
    return vars
 
 
def convert(xml_list, json_file):
    json_dict = {"info":['none'], "license":['none'], "images": [], "annotations": [], "categories": []}
    categories = pre_define_categories.copy()
    bnd_id = START_BOUNDING_BOX_ID
    all_categories = {}
    for index, line in enumerate(xml_list):
        xml_f = line
        tree = ET.parse(xml_f)
        root = tree.getroot()
        filename = os.path.basename(xml_f)[:-4] + ".jpg"
        image_id = index
        size = get_and_check(root, 'size', 1)
        width = int(get_and_check(size, 'width', 1).text)
        height = int(get_and_check(size, 'height', 1).text)
        image = {'file_name': filename, 'height': height, 'width': width, 'id':image_id}
        json_dict['images'].append(image)
        for obj in get(root, 'object'):
            category = get_and_check(obj, 'name', 1).text
            if category in all_categories:
                all_categories[category] += 1
            else:
                all_categories[category] = 1
            if category not in categories:
                if only_care_pre_define_categories:
                    continue
                new_id = len(categories) + 1
                print("[warning] category '{}' not in 'pre_define_categories'({}), create new id: {} automatically".format(category, pre_define_categories, new_id))
                categories[category] = new_id
            category_id = categories[category]
            bndbox = get_and_check(obj, 'bndbox', 1)
            xmin = int(float(get_and_check(bndbox, 'xmin', 1).text))
            ymin = int(float(get_and_check(bndbox, 'ymin', 1).text))
            xmax = int(float(get_and_check(bndbox, 'xmax', 1).text))
            ymax = int(float(get_and_check(bndbox, 'ymax', 1).text))
            assert(xmax > xmin), "xmax <= xmin, {}".format(line)
            assert(ymax > ymin), "ymax <= ymin, {}".format(line)
            o_width = abs(xmax - xmin)
            o_height = abs(ymax - ymin)
            ann = {'area': o_width*o_height, 'iscrowd': 0, 'image_id':
                   image_id, 'bbox':[xmin, ymin, o_width, o_height],
                   'category_id': category_id, 'id': bnd_id, 'ignore': 0,
                   'segmentation': []}
            jso