yolov8改进Neck层怎么修改检测头

修改YOLOv8 Neck层以优化目标检测性能
更换Neck网络为BiFPN
为了提升小目标检测的效果，在YOLOv8中可以考虑将原有的Neck结构替换为双向特征金字塔网络（Bidirectional Feature Pyramid Network, BiFPN）。这种架构能更好地处理多尺度特征，尤其对于小目标而言效果显著[^1]。
from ultralytics import YOLO
import torch.nn as nn

class CustomYOLO(YOLO):
def __init__(self, model='yolov8n.yaml', cfg='', channels=3, classes=80, anchors=None):
super().__init__(model=model, cfg=cfg, channels=channels, classes=classes, anchors=anchors)

# Replace the original neck with a custom implementation of BiFPN here.
self.neck = build_bifpn() # Assuming `build_bifpn` is defined elsewhere.

def build_bifpn():
"""Builds an instance of Bidirectional Feature Pyramid Network."""
bifpn_layers = []

# Define your layers and connections for BiFPN

return nn.Sequential(*bifpn_layers)
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添加小目标检测专用层
除了更改Neck部分外，还可以专门为小目标设计额外的检测层。这些新增加的小目标检测器可以帮助捕捉那些可能被标准检测器忽略掉的对象实例。这一步骤涉及到调整模型配置文件以及编写相应的Python代码来定义新的头部组件。
# yolov8_custom_head.yaml snippet
head:
- type: DetectSmallObjectsHead
args:
num_classes: ${nc}
ch_in: [256, 512, 768]
...
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from typing import List
import torch

class DetectSmallObjectsHead(nn.Module):
def forward(self, x: List[torch.Tensor]):
"""
Forward pass through small object detection head

Args:
x (List[Tensor]): Input feature maps from different levels

Returns:
Tensor or list of Tensors containing predictions on each scale level
"""

outputs = [] # Process input features to produce detections at multiple scales

return outputs
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通过上述方法可以在不改变原有框架的基础上有效增强YOLOv8针对小型物体识别的能力。值得注意的是，当引入像Gold-YOLO这样的改进措施时，GD机制同样有助于加强跨层次间的信息交流，进一步改善整体表现[^2]。