目标检测算法——YOLOv5结合ConvNeXt结构

时间:2022-10-20 15:53:26

目标检测算法——YOLOv5结合ConvNeXt结构

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论文题目:A ConvNet for the 2020s

论文地址:https://arxiv.org/abs/2201.03545

源代码:https://github.com/facebookresearch/ConvNeXt

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MetaAI在论文A ConvNet for the 2020s中, 从ResNet出发并借鉴Swin Transformer提出了一种新的 CNN 模型:ConvNeXt,其效果无论在图像分类还是检测分割任务上均能超过Swin Transformer,而且ConvNeXt和vision transformer一样具有类似的scalability(随着数据量和模型大小增加,性能同比提升)。

目标检测算法——YOLOv5结合ConvNeXt结构

ConvNeXt 从原始的 ResNet 出发,逐步加入swin transform 的 trick,来改进模型。论文中适用 ResNet模型:ResNet50和ResNet200。其中ResNet50和Swin-T有类似的FLOPs(4G vs 4.5G),而ResNet200和Swin-B有类似的FLOPs(15G)。首先做的改进是调整训练策略,然后是模型设计方面的递进优化:宏观设计->ResNeXt化->改用Inverted bottleneck->采用large kernel size->微观设计。由于模型性能和FLOPs强相关,所以在优化过程中尽量保持FLOPs的稳定。

目标检测算法——YOLOv5结合ConvNeXt结构

相关代码:

class ConvNeXt(nn.Module):
    r""" ConvNeXt
        A PyTorch impl of : `A ConvNet for the 2020s`  -
          https://arxiv.org/pdf/2201.03545.pdf

    Args:
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
        dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
        drop_path_rate (float): Stochastic depth rate. Default: 0.
        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
    """
    def __init__(self, in_chans=3, num_classes=1000,
                 depths=[3, 3, 9, 3], dims: list = [96, 192, 384, 768], drop_path_rate=0.,
                 layer_scale_init_value=1e-6, head_init_scale=1.,
                 ):
        super().__init__()

        self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
        stem = nn.Sequential(
            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
            LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
        )
        self.downsample_layers.append(stem)
        for i in range(3):
            downsample_layer = nn.Sequential(
                    LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
                    # 下采样
                    nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
            )
            self.downsample_layers.append(downsample_layer)
        # 4 feature resolution stages, each consisting of multiple residual blocks
        self.stages = nn.ModuleList()
        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
        cur = 0
        for i in range(4):
            stage = nn.Sequential(
                *[Block(dim=dims[i], drop_path=dp_rates[cur + j],
                        layer_scale_init_value=layer_scale_init_value)
                  for j in range(depths[i])]
            )
            self.stages.append(stage)
            cur += depths[i]

        self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer
        self.head = nn.Linear(dims[-1], num_classes)

        self.apply(self._init_weights)
        self.head.weight.data.mul_(head_init_scale)
        self.head.bias.data.mul_(head_init_scale)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=.02)
            nn.init.constant_(m.bias, 0)

    def forward_features(self, x):
        for i in range(4):
            x = self.downsample_layers[i](x)
            x = self.stages[i](x)
        return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x

通过借鉴Swin Transformer的设计来逐步地改进模型。论文共选择了两个不同大小的ResNet模型:ResNet50和ResNet200,其中ResNet50和Swin-T有类似的FLOPs(4G vs 4.5G),而ResNet200和Swin-B有类似的FLOPs(15G)。首先做的改进是调整训练策略,然后是模型设计方面的递进优化:宏观设计>ResNeXt化>改用Inverted bottleneck>采用large kernel size>微观设计。由于模型性能和FLOPs强相关,所以在优化过程中尽量保持FLOPs的稳定。 ​ConVNeXt 这篇文章,通过借鉴 Swin TransForm 精心构建的 tricks,卷积在图像领域反超 Transformerer。

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