目标检测盒残差神经网络

目标检测盒残差神经网络是一种基于ResNet的目标检测算法，它在ResNet的基础上增加了目标检测的头部网络，用于检测图像中的目标物体。该算法的核心思想是在ResNet的基础上增加一个目标检测的头部网络，用于检测图像中的目标物体。具体来说，它在ResNet的最后一层卷积层之后增加了一个RoI Pooling层，用于将不同大小的目标物体映射到相同大小的特征图上，然后再通过全连接层和softmax层进行目标分类和位置回归。

以下是目标检测盒残差神经网络的实现代码：

import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.resnet import resnet50

class BoxResNet(nn.Module):
    def __init__(self, num_classes):
        super(BoxResNet, self).__init__()
        self.resnet = resnet50(pretrained=True)
        self.roi_pool = nn.AdaptiveMaxPool2d((7, 7))
        self.fc1 = nn.Linear(2048 * 7 * 7, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.cls_score = nn.Linear(4096, num_classes)
        self.bbox_pred = nn.Linear(4096, num_classes * 4)

    def forward(self, x, rois):
        x = self.resnet.conv1(x)
        x = self.resnet.bn1(x)
        x = self.resnet.relu(x)
        x = self.resnet.maxpool(x)

        x = self.resnet.layer1(x)
        x = self.resnet.layer2(x)
        x = self.resnet.layer3(x)
        x = self.resnet.layer4(x)

        x = self.roi_pool(x, rois)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))

        cls_score = self.cls_score(x)
        bbox_pred = self.bbox_pred(x)

        return cls_score, bbox_pred