不废话了，直接上代码：

def load_imagepath_from_csv(csv_name):
    image_path = []
    with open(csv_name,'r') as file:
        csv_reader = csv.reader(file)
        next(csv_reader)
        for row in csv_reader:
            image_path.append(row[0])
    return image_path

import csv
csv_name = "submission_demo.csv"     #文件名仅供参考
image_path_list = load_imagepath_from_csv(csv_name)

image_path_list

from MMEdu import MMClassification as cls
import os
import csv
import numpy as np
rootpath = "test_image"     #文件名仅供参考
csv_name = "submission_demo.csv"     #文件名仅供参考
image_path_list = load_imagepath_from_csv(csv_name)
model = cls(backbone='MobileNet')     #MobileNet也可以换成LeNet,ResNet18,ResNet50,RandForest等
checkpoint = 'checkpoints/cls_model/catsdogs_mobilenet_continue/best_accuracy_top-1_epoch_2.pth'     #文件名仅供参考
predictions = []
for image_name in image_path_list:
    image_path = rootpath+'/'+image_name
    y_test_pred = model.inference(image=image_path, show=False, checkpoint=checkpoint, device='cpu')
    y_test_pred = model.print_result(y_test_pred)
    predictions.append(y_test_pred)

predictions

import csv
result_csv_path = 'inference_result/results1.csv'
with open(result_csv_path,"w",newline='') as csvfile:
    csv_writer = csv.writer(csvfile)
    csv_writer.writerow([f'filename','prediction','pre_class'])
    for index.image_name in enumerate(image_path_list):
        csv_writer.writerow([image_name,predictions[index][0]['标签'],predictions[index][0]['预测结果']])
    csvfile.close()

import pandas as pd
df = pd.read_csv(result_csv_path, header=None)
df

 下面是数据集（你也可以自己去网上搜图片）：

 这仅仅是MMEdu在图像分类上的一小部分作用，其他功能待大家发现！

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本文内容为小编自己汇总，内容可能会有错误或疏漏，感谢大家的提议！

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