yolov8添加swin

要在YOLOv8的neck中添加Swin Transformer块，可以参考以下代码示例：

import torch.nn as nn

# 在YOLOv8的neck中引入Swin Transformer块
class YOLOv8(nn.Module):
    def __init__(self, num_classes):
        super(YOLOv8, self).__init__()
        # ...
        self.neck = nn.Sequential(
            nn.Conv2d(1280, 256, kernel_size=1, stride=1, padding=0),
            SwinTransformerBlock(dim=256, num_heads=8, window_size=14),  # 添加Swin Transformer块
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
        )
        self.num_classes = num_classes
        # ...

    def forward(self, x):
        # ...
        x = self.neck(x)
        # ...
        return x

通过在YOLOv8的neck中引入Swin Transformer块，可以融合多尺度特征，有助于模型更好地理解不同大小的目标并提升检测性能。将Swin Transformer V2网络结构与YOLOv8相结合，可以在backbone和neck中引入Swin Transformer块，从而有效提升目标检测的精度和mAP。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [【YOLOv8改进】Swin Transformer V2网络结构与YOLOv8相结合](https://blog.csdn.net/crasher123/article/details/132132967)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 100%"]
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