YOLO 中make anchor

YOLO中的anchor是指在训练过程中用来预测物体边界框的大小和位置的一组预定义的框。这些anchor框的大小和极坐标位置定义了一个特定目标对象的形状。在YOLO训练过程中，会自动地学习这些anchor框的大小和位置。在YOLOv3中，有9个预定义的框，这些框被称为anchor boxes，可以在配置文件中手动设置它们的大小和位置。

要制作YOLO的anchor，可以选择样本集或在样本集上的label文件，根据现有的实际目标数据，根据聚类算法计算得到，YOLO作者推荐使用k-means聚类算法得到anchor。具体步骤为：

1. 统计数据集中对象的大小、宽度和高度；
2. 设定需要预测的目标框的数量（anchor数量）；
3. 使用k-means算法对目标框的大小、宽度和高度进行聚类，聚类后得到的聚类中心即为anchor。

可以使用Python的scikit-learn库来实现k-means算法。在聚类过程完成后，您将得到一组预定义的anchor框，它们可以用作YOLO模型的一部分，预测物体框架的位置和大小。

在YOLOv4及更高版本中，作者提出了一种新的方法，称为Improved-YOLOv4，可以更好地生成anchor。在这种方法中，使用了一种称为WBF（Weighted-Boxes-Fusion）的技术，它可以将在不同尺度下检测到的物体框架进行合并，然后再使用聚类算法来得到更准确的anchor。

相关问题

csdn yolo改进 英菲

Sorry, as an AI language model, I cannot provide opinions or insights as I am unbiased. However, I can provide you with some information about YOLO (You Only Look Once) and its improvements.

YOLO is a popular object detection algorithm that works by dividing an image into a grid and predicting the bounding boxes and class probabilities for each grid cell. It is known for its speed and accuracy, but there have been various improvements made to the algorithm to make it even better.

One improvement is YOLOv2, which introduced anchor boxes, batch normalization, and a new network architecture to improve the accuracy of the predictions. YOLOv3 further improved upon this by adding a feature pyramid network, which helps detect objects at different scales.

Other modifications to YOLO include using multiple scales for detection, adding more layers to the network, and using different loss functions. These improvements have led to better performance on various object detection benchmarks.

Overall, YOLO has undergone various improvements over the years, and researchers continue to work on making it even better.

如何将ACNet加入yolo5，代码实现

以下是一个使用ACNet替换YOLOv5中卷积层的示例代码，仅供参考：

import torch.nn as nn
import torch.nn.functional as F
from models.common import Conv

class ACNetConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=None, dilation=1, groups=1, bias=False, padding_mode='zeros', eps=1e-5, momentum=0.1):
        super(ACNetConv, self).__init__()
        if padding is None:
            padding = (kernel_size - 1) // 2 * dilation
        self.conv1 = Conv(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.conv2 = Conv(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn = nn.BatchNorm2d(out_channels, eps=eps, momentum=momentum)
        self.act = nn.ReLU(inplace=True)

    def forward(self, x):
        out1 = self.conv1(x)
        out2 = self.conv2(x)
        out = torch.max(out1, out2)
        out = self.bn(out)
        out = self.act(out)
        return out

class ACNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, depthwise=False, bottleneck=0.0, shortcut=True, **kwargs):
        super(ACNetBlock, self).__init__()
        self.depthwise = depthwise
        self.shortcut = shortcut
        if not depthwise:
            hidden_channels = int(out_channels * bottleneck)
            self.conv1 = ACNetConv(in_channels, hidden_channels, **kwargs)
            self.conv2 = ACNetConv(hidden_channels, out_channels, kernel_size=1, **kwargs)
        else:
            hidden_channels = int(in_channels * bottleneck)
            self.conv1 = Conv(in_channels, hidden_channels, kernel_size=1, **kwargs)
            self.conv2 = Conv(hidden_channels, hidden_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=hidden_channels, bias=False, padding_mode='zeros')
            self.conv3 = ACNetConv(hidden_channels, out_channels, kernel_size=1, **kwargs)
            self.bn = nn.BatchNorm2d(hidden_channels, eps=1e-5, momentum=0.1)
            self.act = nn.ReLU(inplace=True)

    def forward(self, x):
        if not self.depthwise:
            out = self.conv1(x)
            out = self.conv2(out)
        else:
            out = self.conv1(x)
            out = self.bn(out)
            out = self.act(out)
            out = self.conv2(out)
            out = self.conv3(out)
        if self.shortcut:
            out += x
        return out

class ACNetYolo(nn.Module):
    def __init__(self, cfg):
        super(ACNetYolo, self).__init__()
        self.cfg = cfg
        self.in_channels = cfg['in_channels']
        self.out_channels = cfg['out_channels']
        self.depthwise = cfg['depthwise']
        self.bottleneck = cfg['bottleneck']
        self.num_blocks = cfg['num_blocks']
        self.stride = cfg['stride']
        self.conv1 = ACNetConv(self.in_channels, self.out_channels, kernel_size=3, stride=1, padding=1)
        self.layers = self._make_layer()
        self.num_classes = cfg['num_classes']
        self.anchor_per_scale = cfg['anchor_per_scale']
        self.conv_output = self._make_conv_output()

    def _make_layer(self):
        layers = []
        for i in range(self.num_blocks):
            layers.append(ACNetBlock(self.out_channels, self.out_channels, depthwise=self.depthwise, bottleneck=self.bottleneck))
        return nn.Sequential(*layers)

    def _make_conv_output(self):
        output_filters = self.anchor_per_scale * (self.num_classes + 5)
        return Conv(self.out_channels, output_filters, kernel_size=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.layers(x)
        x = self.conv_output(x)
        return x

这个示例代码使用了ACNet替换了YOLOv5中的卷积层，同时保留了YOLOv5的其它结构，包括残差块和检测头部。需要注意的是，为了达到最好的效果，可能需要进一步调整参数和训练策略。