resnet50医学图像目标检测

对于使用resnet50来测试医学图像数据集，您可以按照以下步骤进行操作：

1. 导入所需的库和模块：

import torch
from torchvision import models, transforms
import torch.nn as nn
import cv2

2. 定义类别标签和图像转换：

classes = ["光化角质病","基底细胞癌","皮肤纤维瘤","黑素瘤","痣","色素良性病","脂溢性角化病","鳞状细胞癌","血管损害"]
transf = transforms.ToTensor()

3. 加载预训练的resnet50模型并设置为不可训练：

resnet50 = models.resnet50(pretrained=True)
for param in resnet50.parameters():
    param.requires_grad = False

4. 修改resnet50的全连接层部分，并加载已经训练好的模型：

fc_inputs = resnet50.fc.in_features
resnet50.fc = nn.Sequential(
    nn.Linear(fc_inputs, 256),
    nn.ReLU(),
    nn.Dropout(0.4),
    nn.Linear(256, len(classes)),
    nn.LogSoftmax(dim=1)
)
model_path = "models/data_model_3.pt"
resnet50 = torch.load(model_path)

5. 加载待测试的医学图像数据集并进行预测：

image_input = cv2.imread("test_image1.jpg")
image_input = transf(image_input)
image_input = torch.unsqueeze(image_input,dim=