详细说明shufflenet v2做图像识别和目标检测时的差别在哪里，并给出有代码解释的代码

ShuffleNet V2是一种轻量级的卷积神经网络，其主要目的是在保持高精度的同时减少模型的计算和内存消耗。相比于传统的卷积神经网络，ShuffleNet V2在计算效率和精度之间取得了良好的平衡。

在图像识别任务中，ShuffleNet V2相比于传统的卷积神经网络，主要的区别在于其采用了两种新的结构：逐通道组卷积和通道重排。逐通道组卷积将卷积操作分解成两个步骤，首先对每个通道进行卷积，然后将不同通道的结果合并在一起。这样可以减少模型中参数的数量，并且可以在一定程度上提高计算效率。通道重排则是通过对输入特征图进行通道的重新排列，使得不同卷积层之间可以共享计算，从而进一步减少计算量。

在目标检测任务中，ShuffleNet V2相比于传统的卷积神经网络，主要的区别在于其采用了轻量级的检测头部结构。具体来说，ShuffleNet V2在检测头部中使用了轻量级的特征金字塔网络和轻量级的预测网络，这样可以在保持较高的检测精度的同时，进一步减少计算量和内存消耗。

以下是使用 PyTorch 实现的 ShuffleNet V2 的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ShuffleNetV2Block(nn.Module):
    def __init__(self, inp, oup, mid_channels, ksize, stride):
        super(ShuffleNetV2Block, self).__init__()
        self.stride = stride

        self.conv1 = nn.Conv2d(inp, mid_channels, 1, 1, 0, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_channels)

        self.depthwise_conv2 = nn.Conv2d(mid_channels, mid_channels, ksize, stride, ksize//2, groups=mid_channels, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_channels)

        self.conv3 = nn.Conv2d(mid_channels, oup, 1, 1, 0, bias=False)
        self.bn3 = nn.BatchNorm2d(oup)

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.depthwise_conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.stride == 2:
            residual = F.avg_pool2d(residual, 2)

        if residual.shape[1] != out.shape[1]:
            residual = torch.cat([residual, residual*0], dim=1)

        out += residual
        out = self.relu(out)

        return out

class ShuffleNetV2(nn.Module):
    def __init__(self, input_size=224, num_classes=1000, scale_factor=1.0):
        super(ShuffleNetV2, self).__init__()

        assert input_size % 32 == 0

        self.stage_repeats = [4, 8, 4]
        self.scale_factor = scale_factor

        # stage 1
        output_channel = self._make_divisible(24 * scale_factor, 4)
        self.conv1 = nn.Conv2d(3, output_channel, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(output_channel)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # stage 2 - 4
        self.stage2 = self._make_stage(2, output_channel, self._make_divisible(48 * scale_factor, 4), 3, 2)
        self.stage3 = self._make_stage(self.stage_repeats[0], self._make_divisible(48 * scale_factor, 4), self._make_divisible(96 * scale_factor, 4), 3, 2)
        self.stage4 = self._make_stage(self.stage_repeats[1], self._make_divisible(96 * scale_factor, 4), self._make_divisible(192 * scale_factor, 4), 3, 2)

        # stage 5
        self.stage5 = nn.Sequential(
            nn.Conv2d(self._make_divisible(192 * scale_factor, 4), self._make_divisible(1024 * scale_factor, 4), kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(self._make_divisible(1024 * scale_factor, 4)),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((1, 1)),
        )

        # classifier
        self.fc = nn.Linear(self._make_divisible(1024 * scale_factor, 4), num_classes)

        self._initialize_weights()

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

        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.stage5(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

    def _make_divisible(self, v, divisor, min_value=None):
        if min_value is None:
            min_value = divisor
        new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
        # Make sure that round down does not go down by more than 10%.
        if new_v < 0.9 * v:
            new_v += divisor
        return new_v

    def _make_stage(self, repeat_num, inp, oup, ksize, stride):
        layers = []
        layers.append(ShuffleNetV2Block(inp, oup, oup//2, ksize, stride))

        for i in range(repeat_num):
            layers.append(ShuffleNetV2Block(oup, oup, oup//2, ksize, 1))

        return nn.Sequential(*layers)

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

以上代码实现了一个基于 ShuffleNet V2 的图像分类模型。其中 `_make_stage` 方法用于构造网络中的每个 stage，而 `ShuffleNetV2Block` 则是构造每个 stage 中的基本单元。在实现目标检测任务时，可以将这个模型作为特征提取器，在此基础上添加轻量级的检测头部结构即可。