1，本文介绍
MSDA（多尺度扩张注意力）模块通过自注意力机制在不同尺度上有效地捕捉特征的稀疏性。它首先通过线性投影生成特征图 (X) 的查询、键和值。然后，将特征图的通道划分为 (n) 个头部，在每个头部中使用不同的扩张率进行多尺度的自注意力操作。具体来说，MSDA按以下步骤操作：对每个头部 (i) 进行自注意力处理，并将所有头部的输出连接在一起，之后通过线性层进行特征融合。通过为不同头部设置不同的扩张率，MSDA可以在关注的接收域内有效地聚合多尺度的语义信息，同时在避免复杂操作和额外计算成本的情况下，减少了自注意力机制的冗余。
MSDA模块的主要改进包括：
-
多尺度特征提取：通过不同头部的自注意力机制，MSDA能够捕捉到不同尺度的语义信息，这对于理解图像的不同抽象层次非常重要。
-
稀疏性利用：MSDA利用自注意力机制在不同尺度上的稀疏性，降低了计算冗余，同时保持了良好的性能。
-
头部通道分离：MSDA将特征图的通道分割为多个头部，每个头部处理不同的特征子集，这样可以并行处理，提升模型的学习能力和效率。
-
不同扩张率：通过在不同头部设置不同的扩张率，MSDA能够在各个头部关注不同尺度的特征，从而更全面地捕捉图像中的信息。
-
特征聚合：MSDA将各个头部的输出通过连接操作合并，并通过线性层进行特征聚合，整合各个头部学习到的信息，得到更丰富的特征表示。
关于MSDA的详细介绍可以看论文：https://arxiv.org/pdf/2302.01791.pdf
本文将讲解如何将MSDA融合进yolov8
话不多说，上代码！
２，将MSDA融合进YOLＯv8
2.1 步骤一
首先找到如下的目录'ultralytics/nn/modules'，然后在这个目录下创建一个MSDA.py文件，文件名字可以根据你自己的习惯起，然后将MSDA的核心代码复制进去。
import torch
import torch.nn as nn
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class DilateAttention(nn.Module):
"Implementation of Dilate-attention"
def __init__(self, head_dim, qk_scale=None, attn_drop=0, kernel_size=3, dilation=1):
super().__init__()
self.head_dim = head_dim
self.scale = qk_scale or head_dim ** -0.5
self.kernel_size=kernel_size
self.unfold = nn.Unfold(kernel_size, dilation, dilation*(kernel_size-1)//2, 1)
self.attn_drop = nn.Dropout(attn_drop)
def forward(self,q,k,v):
#B, C//3, H, W
B,d,H,W = q.shape
q = q.reshape([B, d//self.head_dim, self.head_dim, 1 ,H*W]).permute(0, 1, 4, 3, 2) # B,h,N,1,d
k = self.unfold(k).reshape([B, d//self.head_dim, self.head_dim, self.kernel_size*self.kernel_size, H*W]).permute(0, 1, 4, 2, 3) #B,h,N,d,k*k
attn = (q @ k) * self.scale # B,h,N,1,k*k
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
v = self.unfold(v).reshape([B, d//self.head_dim, self.head_dim, self.kernel_size*self.kernel_size, H*W]).permute(0, 1, 4, 3, 2) # B,h,N,k*k,d
x = (attn @ v).transpose(1, 2).reshape(B, H, W, d)
return x
class MultiDilatelocalAttention(nn.Module):
"Implementation of Dilate-attention"
def __init__(self, dim, num_heads=8, qkv_bias=True, qk_scale=None,
attn_drop=0.,proj_drop=0., kernel_size=3, dilation=[1, 2, 3, 4]):
super().__init__()
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.dilation = dilation
self.kernel_size = kernel_size
self.scale = qk_scale or head_dim ** -0.5
self.num_dilation = len(dilation)
assert num_heads % self.num_dilation == 0, f"num_heads{num_heads} must be the times of num_dilation{self.num_dilation}!!"
self.qkv = nn.Conv2d(dim, dim * 3, 1, bias=qkv_bias)
self.dilate_attention = nn.ModuleList(
[DilateAttention(head_dim, qk_scale, attn_drop, kernel_size, dilation[i])
for i in range(self.num_dilation)])
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, C, H, W = x.shape
# x = x.permute(0, 3, 1, 2)# B, C, H, W
y = x.clone()
qkv = self.qkv(x).reshape(B, 3, self.num_dilation, C//self.num_dilation, H, W).permute(2, 1, 0, 3, 4, 5)
#num_dilation,3,B,C//num_dilation,H,W
y1 = y.reshape(B, self.num_dilation, C//self.num_dilation, H, W).permute(1, 0, 3, 4, 2 )
# num_dilation, B, H, W, C//num_dilation
for i in range(self.num_dilation):
y1[i] = self.dilate_attention[i](qkv[i][0], qkv[i][1], qkv[i][2])# B, H, W,C//num_dilation
y2 = y1.permute(1, 2, 3, 0, 4).reshape(B, H, W, C)
y3 = self.proj(y2)
y4 = self.proj_drop(y3).permute(0, 3, 1, 2)
return y4
class DilateBlock(nn.Module):
"Implementation of Dilate-attention block"
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False,qk_scale=None, drop=0., attn_drop=0.,
drop_path=0.,act_layer=nn.GELU, norm_layer=nn.LayerNorm, kernel_size=3, dilation=[1, 2, 3],
cpe_per_block=False):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.kernel_size = kernel_size
self.dilation = dilation
self.cpe_per_block = cpe_per_block
if self.cpe_per_block:
self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
self.norm1 = norm_layer(dim)
self.attn = MultiDilatelocalAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, kernel_size=kernel_size, dilation=dilation)
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
act_layer=act_layer, drop=drop)
def forward(self, x):
if self.cpe_per_block:
x = x + self.pos_embed(x)
x = x.permute(0, 2, 3, 1)
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
x = x.permute(0, 3, 1, 2)
#B, C, H, W
return x
class GlobalAttention(nn.Module):
"Implementation of self-attention"
def __init__(self, dim, num_heads=8, qkv_bias=False,
qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, H, W, C = x.shape
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads,
C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, H, W, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class GlobalBlock(nn.Module):
"""
Implementation of Transformer
"""
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False,qk_scale=None, drop=0.,
attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
cpe_per_block=False):
super().__init__()
self.cpe_per_block = cpe_per_block
if self.cpe_per_block:
self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
self.norm1 = norm_layer(dim)
self.attn = GlobalAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias,
qk_scale=qk_scale, attn_drop=attn_drop)
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
act_layer=act_layer, drop=drop)
def forward(self, x):
if self.cpe_per_block:
x = x + self.pos_embed(x)
x = x.permute(0, 2, 3, 1)
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
x = x.permute(0, 3, 1, 2)
return x
class PatchEmbed(nn.Module):
"""Image to Patch Embedding.
"""
def __init__(self, img_size=224, in_chans=3, hidden_dim=16,
patch_size=4, embed_dim=96, patch_way=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.img_size = img_size
assert patch_way in ['overlaping', 'nonoverlaping', 'pointconv'],\
"the patch embedding way isn't exist!"
if patch_way == "nonoverlaping":
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
elif patch_way == "overlaping":
self.proj = nn.Sequential(
nn.Conv2d(in_chans, hidden_dim, kernel_size=3, stride=1,
padding=1, bias=False), # 224x224
nn.BatchNorm2d(hidden_dim),
nn.GELU( ),
nn.Conv2d(hidden_dim, int(hidden_dim*2), kernel_size=3, stride=2,
padding=1, bias=False), # 112x112
nn.BatchNorm2d(int(hidden_dim*2)),
nn.GELU( ),
nn.Conv2d(int(hidden_dim*2), int(hidden_dim*4), kernel_size=3, stride=1,
padding=1, bias=False), # 112x112
nn.BatchNorm2d(int(hidden_dim*4)),
nn.GELU( ),
nn.Conv2d(int(hidden_dim*4), embed_dim, kernel_size=3, stride=2,
padding=1, bias=False), # 56x56
)
else:
self.proj = nn.Sequential(
nn.Conv2d(in_chans, hidden_dim, kernel_size=3, stride=2,
padding=1, bias=False), # 112x112
nn.BatchNorm2d(hidden_dim),
nn.GELU( ),
nn.Conv2d(hidden_dim, int(hidden_dim*2), kernel_size=1, stride=1,
padding=0, bias=False), # 112x112
nn.BatchNorm2d(int(hidden_dim*2)),
nn.GELU( ),
nn.Conv2d(int(hidden_dim*2), int(hidden_dim*4), kernel_size=3, stride=2,
padding=1, bias=False), # 56x56
nn.BatchNorm2d(int(hidden_dim*4)),
nn.GELU( ),
nn.Conv2d(int(hidden_dim*4), embed_dim, kernel_size=1, stride=1,
padding=0, bias=False), # 56x56
)
def forward(self, x):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x) # B, C, H, W
return x
class PatchMerging(nn.Module):
""" Patch Merging Layer.
"""
def __init__(self, in_channels, out_channels, merging_way, cpe_per_satge, norm_layer=nn.BatchNorm2d):
super().__init__()
assert merging_way in ['conv3_2', 'conv2_2', 'avgpool3_2', 'avgpool2_2'], \
"the merging way is not exist!"
self.cpe_per_satge = cpe_per_satge
if merging_way == 'conv3_2':
self.proj = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1),
norm_layer(out_channels),
)
elif merging_way == 'conv2_2':
self.proj = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=2, stride=2, padding=0),
norm_layer(out_channels),
)
elif merging_way == 'avgpool3_2':
self.proj = nn.Sequential(
nn.AvgPool2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1),
norm_layer(out_channels),
)
else:
self.proj = nn.Sequential(
nn.AvgPool2d(in_channels, out_channels, kernel_size=2, stride=2, padding=0),
norm_layer(out_channels),
)
if self.cpe_per_satge:
self.pos_embed = nn.Conv2d(out_channels, out_channels, 3, padding=1, groups=out_channels)
def forward(self, x):
#x: B, C, H ,W
x = self.proj(x)
if self.cpe_per_satge:
x = x + self.pos_embed(x)
return x
class Dilatestage(nn.Module):
""" A basic Dilate Transformer layer for one stage.
"""
def __init__(self, dim, depth, num_heads, kernel_size, dilation,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0.,
attn_drop=0., drop_path=0., act_layer=nn.GELU,
norm_layer=nn.LayerNorm, cpe_per_satge=False, cpe_per_block=False,
downsample=True, merging_way=None):
super().__init__()
# build blocks
self.blocks = nn.ModuleList([
DilateBlock(dim=dim, num_heads=num_heads,
kernel_size=kernel_size, dilation=dilation,
mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
qk_scale=qk_scale, drop=drop, attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer, act_layer=act_layer, cpe_per_block=cpe_per_block)
for i in range(depth)])
# patch merging layer
self.downsample = PatchMerging(dim, int(dim * 2), merging_way, cpe_per_satge) if downsample else nn.Identity()
def forward(self, x):
for blk in self.blocks:
x = blk(x)
x = self.downsample(x)
return x
class Globalstage(nn.Module):
""" A basic Transformer layer for one stage."""
def __init__(self, dim, depth, num_heads, mlp_ratio=4., qkv_bias=True, qk_scale=None,
drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
cpe_per_satge=False, cpe_per_block=False,
downsample=True, merging_way=None):
super().__init__()
# build blocks
self.blocks = nn.ModuleList([
GlobalBlock(dim=dim, num_heads=num_heads,
mlp_ratio=mlp_ratio,qkv_bias=qkv_bias,
qk_scale=qk_scale, drop=drop, attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer, act_layer=act_layer, cpe_per_block=cpe_per_block)
for i in range(depth)])
# patch merging layer
self.downsample = PatchMerging(dim, int(dim*2), merging_way, cpe_per_satge) if downsample else nn.Identity()
def forward(self, x):
for blk in self.blocks:
x = blk(x)
x = self.downsample(x)
return x
class Dilateformer(nn.Module):
def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96,
depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], kernel_size=3, dilation=[1, 2, 3],
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.1,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
merging_way='conv3_2',
patch_way='overlaping',
dilate_attention=[True, True, False, False],
downsamples=[True, True, True, False],
cpe_per_satge=False, cpe_per_block=True):
super().__init__()
self.num_classes = num_classes
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.mlp_ratio = mlp_ratio
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
#patch embedding
self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size,
in_chans=in_chans, embed_dim=embed_dim, patch_way=patch_way)
dpr = [x.item() for x in torch.linspace(0, drop_path, sum(depths))]
self.stages = nn.ModuleList()
for i_layer in range(self.num_layers):
if dilate_attention[i_layer]:
stage = Dilatestage(dim=int(embed_dim * 2 ** i_layer),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
kernel_size=kernel_size,
dilation=dilation,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=downsamples[i_layer],
cpe_per_block=cpe_per_block,
cpe_per_satge=cpe_per_satge,
merging_way=merging_way
)
else:
stage = Globalstage(dim=int(embed_dim * 2 ** i_layer),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=downsamples[i_layer],
cpe_per_block=cpe_per_block,
cpe_per_satge=cpe_per_satge,
merging_way=merging_way
)
self.stages.append(stage)
self.norm = norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'absolute_pos_embed'}
def forward_features(self, x):
x = self.patch_embed(x)
for stage in self.stages:
x = stage(x)
x = x.flatten(2).transpose(1, 2)
x = self.norm(x) # B L C
x = self.avgpool(x.transpose(1, 2)) # B C 1
x = torch.flatten(x, 1)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
@register_model
def dilateformer_tiny(pretrained=True, **kwargs):
model = Dilateformer(depths=[2, 2, 6, 2], embed_dim=72, num_heads=[ 3, 6, 12, 24 ], **kwargs)
model.default_cfg = _cfg()
return model
@register_model
def dilateformer_small(pretrained=True, **kwargs):
model = Dilateformer(depths=[3, 5, 8, 3], embed_dim=72, num_heads=[ 3, 6, 12, 24 ], **kwargs)
model.default_cfg = _cfg()
return model
@register_model
def dilateformer_base(pretrained=True, **kwargs):
model = Dilateformer(depths=[4, 8, 10, 3], embed_dim=96, num_heads=[ 3, 6, 12, 24 ], **kwargs)
model.default_cfg = _cfg()
return model
if __name__ == "__main__":
x = torch.rand([1, 3, 224,224])
m = dilateformer_tiny(pretrained=False)
y = m(x)
print(y.shape)
2.2 步骤二
在tasks.py中注册我们的MSDA模块。 如下图所示
2.3 步骤三
在parse_model中添加如下红框标注代码
到此注册成功,复制后面的yaml文件直接运行即可
关于msda添加的位置有两种方案，大家可以自行选择
yaml文件1
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
- [-1, 1, MultiDilatelocalAttention, []] # 22
- [[15, 18, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)
yaml文件2
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
- [-1, 1, MultiDilatelocalAttention, []] # 16
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
- [-1, 1, MultiDilatelocalAttention, []] # 20
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 23 (P5/32-large)
- [-1, 1, MultiDilatelocalAttention, []] # 24
- [[16, 20, 24], 1, Detect, [nc]] # Detect(P3, P4, P5)
# 关于MSDA添加的位置可以自行调试,针对不同数据集位置不同,效果不同
不知不觉已经看完了哦，动动小手留个点赞吧--_--