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本文给大家带来的改进机制是MSDA#xff08;多尺度空洞注意力#xff09;发表于今年的中科院一区(算是国内计算机领域的最高期刊了)#xff0c;其全称是DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition。MSDA的主要思想是…一、本文介绍
本文给大家带来的改进机制是MSDA多尺度空洞注意力发表于今年的中科院一区(算是国内计算机领域的最高期刊了)其全称是DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition。MSDA的主要思想是通过线性投影得到特征图X的相应查询、键和值。然后将特征图的通道分成n个不同的头部并在不同的头部中以不同的扩张率执行多尺度SWDA来提高模型的处理效率和检测精度。亲测在小目标检测和大尺度目标检测的数据集上都有大幅度的涨点效果(mAP直接涨了大概有0.06左右)。最后本文会手把手教你添加MSDA模块到网络结构中。
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涨点效果⭐⭐⭐⭐⭐ 专栏回顾YOLOv8改进系列专栏——本专栏持续复习各种顶会内容——科研必备 训练结果对比图-
以下结果我仅在大目标检测层的输出层添加了一个MSDA如果在中等和小目标检测层都添加效果会更好。 目录
一、本文介绍
二、MSDA框架原理
三、MSDA核心代码
四、手把手教你添加MSDA模块
4.1 MSDA添加步骤
4.1.1 步骤一
4.1.2 步骤二
4.1.3 步骤三
4.2 MSDA的yaml文件和训练截图
4.2.1 MSDA的yaml版本一(推荐)
4.2.2 MSDA的yaml版本二
4.3 推荐MSDA可添加的位置
4.4 MSDA的训练过程截图
五、本文总结 二、MSDA框架原理
论文地址官方论文地址点击即可跳转
代码地址官方代码地址点击即可跳转
在DilateFormer论文中多尺度扩张注意力MSDA模块是为了利用自注意机制在不同尺度上的稀疏性。MSDA通过线性投影得到特征图X的相应查询、键和值。然后将特征图的通道分成n个不同的头部并在不同的头部中以不同的扩张率执行多尺度SWDA。具体来说MSDA被公式化如下对于每个头部i进行SWDA操作并且对所有的输出进行连接后送入一个线性层进行特征聚合。通过为不同的头部设置不同的扩张率MSDA能够在被关注的接受域内有效地聚合不同尺度的语义信息并在不需要复杂操作和额外计算成本的情况下有效地减少自注意机制的冗余
MSDA多尺度扩张注意力模块的主要改进机制包括以下几点
1. 多尺度特征提取通过不同头部的自注意力机制MSDA能够捕捉到多尺度的语义信息这对于理解图像的不同抽象层次是非常重要的。
2. 稀疏性利用MSDA利用了自注意力机制在不同尺度的稀疏性降低了计算的冗余同时保持了性能。
3. 头部通道分离MSDA将特征图的通道分离成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增强了模型的学习能力和效率。
4. 不同的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能更加全面地捕捉图像中的信息。
5. 特征聚合MSDA的输出通过连接操作合并并通过线性层进行特征聚合这样可以整合各个头部学习到的信息得到更丰富的特征表示。
这些改进使得MSDA在不增加额外计算成本的情况下提高了自注意力机制的效率和效果。 这幅图展示了ViT-Small的第三个多头自注意力Multi-Head Self-Attention, MHSA块的注意力图的可视化。在每张图中一个特定的查询块红色框内的区域被用来展示其它各个块对它的注意力程度。注意力图显示了具有高注意力得分的块在查询块周围稀疏分布而其它块的注意力得分较低。 这张图展示了多尺度扩张注意力MSDA的工作原理。在MSDA中特征图的通道首先被分割成不同的头部然后每个头部内部使用不同的扩张率dilation ratesr来执行自注意力操作。这些操作在围绕红色查询块的窗口内的彩色块之间进行。
图中的例子展示了三种不同的扩张率r1, 2, 3(这里需要注意的是咱们我的网络中需要改成四种的扩张率)它们分别对应不同的感受野大小3x3, 5x5, 7x7。每个头部的自注意力操作针对的是其对应的扩张率和感受野。这样模型能够在不同的尺度上捕捉图像特征这些特征随后被连接在一起并送入一个线性层进行特征聚合。
这种设计允许模型在不同的尺度上理解图像从而提高对图像内容的整体理解。通过这种方法MSDA不仅可以捕捉局部细节也能够感知到更广泛区域的上下文信息增强了模型的表现力。 三、MSDA核心代码
下面的代码是MSDA的核心代码我们将其复制导ultralytics/nn/modules目录下在其中创建一个文件我这里起名为Dilation然后粘贴进去其余使用方式看章节四。
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
torch.autograd.set_detect_anomaly(True)
class Mlp(nn.Module):def __init__(self, in_features, hidden_featuresNone, out_featuresNone, act_layernn.GELU, drop0.):super().__init__()out_features out_features or in_featureshidden_features hidden_features or in_featuresself.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 xclass DilateAttention(nn.Module):Implementation of Dilate-attentiondef __init__(self, head_dim, qk_scaleNone, attn_drop0, kernel_size3, dilation1):super().__init__()self.head_dim head_dimself.scale qk_scale or head_dim ** -0.5self.kernel_sizekernel_sizeself.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, WB,d,H,W q.shapeq q.reshape([B, d//self.head_dim, self.head_dim, 1 ,H*W]).permute(0, 1, 4, 3, 2) # B,h,N,1,dk 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*kattn (q k) * self.scale # B,h,N,1,k*kattn 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,dx (attn v).transpose(1, 2).reshape(B, H, W, d)return xclass MultiDilatelocalAttention(nn.Module):Implementation of Dilate-attentiondef __init__(self, dim, num_heads8, qkv_biasTrue, qk_scaleNone,attn_drop0.,proj_drop0., kernel_size3, dilation[1, 2, 3, 4]):super().__init__()self.dim dimself.num_heads num_headshead_dim dim // num_headsself.dilation dilationself.kernel_size kernel_sizeself.scale qk_scale or head_dim ** -0.5self.num_dilation len(dilation)assert num_heads % self.num_dilation 0, fnum_heads{num_heads} must be the times of num_dilation{self.num_dilation}!!self.qkv nn.Conv2d(dim, dim * 3, 1, biasqkv_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, Wy 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,Wy1 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_dilationfor 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_dilationy2 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 y4class DilateBlock(nn.Module):Implementation of Dilate-attention blockdef __init__(self, dim, num_heads, mlp_ratio4., qkv_biasFalse,qk_scaleNone, drop0., attn_drop0.,drop_path0.,act_layernn.GELU, norm_layernn.LayerNorm, kernel_size3, dilation[1, 2, 3],cpe_per_blockFalse):super().__init__()self.dim dimself.num_heads num_headsself.mlp_ratio mlp_ratioself.kernel_size kernel_sizeself.dilation dilationself.cpe_per_block cpe_per_blockif self.cpe_per_block:self.pos_embed nn.Conv2d(dim, dim, 3, padding1, groupsdim)self.norm1 norm_layer(dim)self.attn MultiDilatelocalAttention(dim, num_headsnum_heads, qkv_biasqkv_bias, qk_scaleqk_scale,attn_dropattn_drop, kernel_sizekernel_size, dilationdilation)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_featuresdim, hidden_featuresmlp_hidden_dim,act_layeract_layer, dropdrop)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, Wreturn xclass GlobalAttention(nn.Module):Implementation of self-attentiondef __init__(self, dim, num_heads8, qkv_biasFalse,qk_scaleNone, attn_drop0., proj_drop0.):super().__init__()self.num_heads num_headshead_dim dim // num_headsself.scale qk_scale or head_dim**-0.5self.qkv nn.Linear(dim, dim * 3, biasqkv_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.shapeqkv 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.scaleattn 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 xclass GlobalBlock(nn.Module):Implementation of Transformerdef __init__(self, dim, num_heads, mlp_ratio4., qkv_biasFalse,qk_scaleNone, drop0.,attn_drop0., drop_path0., act_layernn.GELU, norm_layernn.LayerNorm,cpe_per_blockFalse):super().__init__()self.cpe_per_block cpe_per_blockif self.cpe_per_block:self.pos_embed nn.Conv2d(dim, dim, 3, padding1, groupsdim)self.norm1 norm_layer(dim)self.attn GlobalAttention(dim, num_headsnum_heads, qkv_biasqkv_bias,qk_scaleqk_scale, attn_dropattn_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_featuresdim, hidden_featuresmlp_hidden_dim,act_layeract_layer, dropdrop)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 xclass PatchEmbed(nn.Module):Image to Patch Embedding.def __init__(self, img_size224, in_chans3, hidden_dim16,patch_size4, embed_dim96, patch_wayNone):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_sizeassert patch_way in [overlaping, nonoverlaping, pointconv],\the patch embedding way isnt exist!if patch_way nonoverlaping:self.proj nn.Conv2d(in_chans, embed_dim, kernel_sizepatch_size, stridepatch_size)elif patch_way overlaping:self.proj nn.Sequential(nn.Conv2d(in_chans, hidden_dim, kernel_size3, stride1,padding1, biasFalse), # 224x224nn.BatchNorm2d(hidden_dim),nn.GELU( ),nn.Conv2d(hidden_dim, int(hidden_dim*2), kernel_size3, stride2,padding1, biasFalse), # 112x112nn.BatchNorm2d(int(hidden_dim*2)),nn.GELU( ),nn.Conv2d(int(hidden_dim*2), int(hidden_dim*4), kernel_size3, stride1,padding1, biasFalse), # 112x112nn.BatchNorm2d(int(hidden_dim*4)),nn.GELU( ),nn.Conv2d(int(hidden_dim*4), embed_dim, kernel_size3, stride2,padding1, biasFalse), # 56x56)else:self.proj nn.Sequential(nn.Conv2d(in_chans, hidden_dim, kernel_size3, stride2,padding1, biasFalse), # 112x112nn.BatchNorm2d(hidden_dim),nn.GELU( ),nn.Conv2d(hidden_dim, int(hidden_dim*2), kernel_size1, stride1,padding0, biasFalse), # 112x112nn.BatchNorm2d(int(hidden_dim*2)),nn.GELU( ),nn.Conv2d(int(hidden_dim*2), int(hidden_dim*4), kernel_size3, stride2,padding1, biasFalse), # 56x56nn.BatchNorm2d(int(hidden_dim*4)),nn.GELU( ),nn.Conv2d(int(hidden_dim*4), embed_dim, kernel_size1, stride1,padding0, biasFalse), # 56x56)def forward(self, x):B, C, H, W x.shape# FIXME look at relaxing size constraintsassert H self.img_size[0] and W self.img_size[1], \fInput image size ({H}*{W}) doesnt match model ({self.img_size[0]}*{self.img_size[1]}).x self.proj(x) # B, C, H, Wreturn xclass PatchMerging(nn.Module): Patch Merging Layer.def __init__(self, in_channels, out_channels, merging_way, cpe_per_satge, norm_layernn.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_satgeif merging_way conv3_2:self.proj nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size3, stride2, padding1),norm_layer(out_channels),)elif merging_way conv2_2:self.proj nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size2, stride2, padding0),norm_layer(out_channels),)elif merging_way avgpool3_2:self.proj nn.Sequential(nn.AvgPool2d(in_channels, out_channels, kernel_size3, stride2, padding1),norm_layer(out_channels),)else:self.proj nn.Sequential(nn.AvgPool2d(in_channels, out_channels, kernel_size2, stride2, padding0),norm_layer(out_channels),)if self.cpe_per_satge:self.pos_embed nn.Conv2d(out_channels, out_channels, 3, padding1, groupsout_channels)def forward(self, x):#x: B, C, H ,Wx self.proj(x)if self.cpe_per_satge:x x self.pos_embed(x)return xclass Dilatestage(nn.Module): A basic Dilate Transformer layer for one stage.def __init__(self, dim, depth, num_heads, kernel_size, dilation,mlp_ratio4., qkv_biasTrue, qk_scaleNone, drop0.,attn_drop0., drop_path0., act_layernn.GELU,norm_layernn.LayerNorm, cpe_per_satgeFalse, cpe_per_blockFalse,downsampleTrue, merging_wayNone):super().__init__()# build blocksself.blocks nn.ModuleList([DilateBlock(dimdim, num_headsnum_heads,kernel_sizekernel_size, dilationdilation,mlp_ratiomlp_ratio, qkv_biasqkv_bias,qk_scaleqk_scale, dropdrop, attn_dropattn_drop,drop_pathdrop_path[i] if isinstance(drop_path, list) else drop_path,norm_layernorm_layer, act_layeract_layer, cpe_per_blockcpe_per_block)for i in range(depth)])# patch merging layerself.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 xclass Globalstage(nn.Module): A basic Transformer layer for one stage.def __init__(self, dim, depth, num_heads, mlp_ratio4., qkv_biasTrue, qk_scaleNone,drop0., attn_drop0., drop_path0., act_layernn.GELU, norm_layernn.LayerNorm,cpe_per_satgeFalse, cpe_per_blockFalse,downsampleTrue, merging_wayNone):super().__init__()# build blocksself.blocks nn.ModuleList([GlobalBlock(dimdim, num_headsnum_heads,mlp_ratiomlp_ratio,qkv_biasqkv_bias,qk_scaleqk_scale, dropdrop, attn_dropattn_drop,drop_pathdrop_path[i] if isinstance(drop_path, list) else drop_path,norm_layernorm_layer, act_layeract_layer, cpe_per_blockcpe_per_block)for i in range(depth)])# patch merging layerself.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 xclass Dilateformer(nn.Module):def __init__(self, img_size224, patch_size4, in_chans3, num_classes1000, embed_dim96,depths[2, 2, 6, 2], num_heads[3, 6, 12, 24], kernel_size3, dilation[1, 2, 3],mlp_ratio4., qkv_biasTrue, qk_scaleNone, drop0., attn_drop0., drop_path0.1,norm_layerpartial(nn.LayerNorm, eps1e-6),merging_wayconv3_2,patch_wayoverlaping,dilate_attention[True, True, False, False],downsamples[True, True, True, False],cpe_per_satgeFalse, cpe_per_blockTrue):super().__init__()self.num_classes num_classesself.num_layers len(depths)self.embed_dim embed_dimself.num_features int(embed_dim * 2 ** (self.num_layers - 1))self.mlp_ratio mlp_rationorm_layer norm_layer or partial(nn.LayerNorm, eps1e-6)#patch embeddingself.patch_embed PatchEmbed(img_sizeimg_size, patch_sizepatch_size,in_chansin_chans, embed_dimembed_dim, patch_waypatch_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(dimint(embed_dim * 2 ** i_layer),depthdepths[i_layer],num_headsnum_heads[i_layer],kernel_sizekernel_size,dilationdilation,mlp_ratioself.mlp_ratio,qkv_biasqkv_bias, qk_scaleqk_scale,dropdrop, attn_dropattn_drop,drop_pathdpr[sum(depths[:i_layer]):sum(depths[:i_layer 1])],norm_layernorm_layer,downsampledownsamples[i_layer],cpe_per_blockcpe_per_block,cpe_per_satgecpe_per_satge,merging_waymerging_way)else:stage Globalstage(dimint(embed_dim * 2 ** i_layer),depthdepths[i_layer],num_headsnum_heads[i_layer],mlp_ratioself.mlp_ratio,qkv_biasqkv_bias, qk_scaleqk_scale,dropdrop, attn_dropattn_drop,drop_pathdpr[sum(depths[:i_layer]):sum(depths[:i_layer 1])],norm_layernorm_layer,downsampledownsamples[i_layer],cpe_per_blockcpe_per_block,cpe_per_satgecpe_per_satge,merging_waymerging_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.ignoredef 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 Cx self.avgpool(x.transpose(1, 2)) # B C 1x torch.flatten(x, 1)return xdef forward(self, x):x self.forward_features(x)x self.head(x)return xregister_model
def dilateformer_tiny(pretrainedTrue, **kwargs):model Dilateformer(depths[2, 2, 6, 2], embed_dim72, num_heads[ 3, 6, 12, 24 ], **kwargs)model.default_cfg _cfg()return modelregister_model
def dilateformer_small(pretrainedTrue, **kwargs):model Dilateformer(depths[3, 5, 8, 3], embed_dim72, num_heads[ 3, 6, 12, 24 ], **kwargs)model.default_cfg _cfg()return modelregister_model
def dilateformer_base(pretrainedTrue, **kwargs):model Dilateformer(depths[4, 8, 10, 3], embed_dim96, num_heads[ 3, 6, 12, 24 ], **kwargs)model.default_cfg _cfg()return modelif __name__ __main__:x torch.rand([1, 3, 224,224])m dilateformer_tiny(pretrainedFalse)y m(x)print(y.shape) 四、手把手教你添加MSDA模块
4.1 MSDA添加步骤
4.1.1 步骤一
首先我们找到如下的目录ultralytics/nn/modules然后在这个目录下创建一个py文件名字为Dilation即可(你也可以根据你自己的习惯起)然后将MSDA的核心代码复制进去。 4.1.2 步骤二
之后我们找到ultralytics/nn/tasks.py文件在其中注册我们的MSDA模块。
首先我们需要在文件的开头导入我们的MSDA模块如下图所示-
4.1.3 步骤三
我们找到parse_model这个方法可以用搜索也可以自己手动找大概在六百多行吧。 我们找到如下的地方然后将MSDA添加进去即可模仿我添加即可其中的两外两个模块你没有删除即可。
到此我们就注册成功了可以修改yaml文件中输入MSDA使用这个模块了。 4.2 MSDA的yaml文件和训练截图
下面推荐几个版本的yaml文件给大家大家可以复制进行训练但是组合用很多具体那种最有效果都不一定针对不同的数据集效果也不一样我不可每一种都做实验所以我下面推荐了几种我自己认为可能有效果的配合方式你也可以自己进行组合。 4.2.1 MSDA的yaml版本一(推荐)
下面的添加MSDA是我实验结果的版本我仅在大目标检测层的输出添加了一个MSDA模块就涨点了0.05左右所以大家可以在中等和小目标检测层都添加MSDA模块进行尝试下面的yaml文件我会给大家推荐。
(需要注意训练这个MSDA的时候需要把amp关掉amp关掉amp关掉) # 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. modelyolov8n.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 GFLOPss: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPsm: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPsl: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [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, RCSOSA, [128, True]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, RCSOSA, [256, True]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 6, RCSOSA, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 3, RCSOSA, [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, RCSOSA, [512]] # 12- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, RCSOSA, [256]] # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]] # cat head P4- [-1, 3, RCSOSA, [512]] # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]] # cat head P5- [-1, 3, RCSOSA, [1024]] # 21 (P5/32-large)- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)4.2.2 MSDA的yaml版本二
添加的版本二具体那种适合你需要大家自己多做实验来尝试。
(需要注意训练这个MSDA的时候需要把amp关掉amp关掉amp关掉) # 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. modelyolov8n.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 GFLOPss: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPsm: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPsl: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [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)4.3 推荐MSDA可添加的位置
MSDA是一种即插即用的可替换卷积的模块其可以添加的位置有很多添加的位置不同效果也不同所以我下面推荐几个添加的位置大家可以进行参考当然不一定要按照我推荐的地方添加。 残差连接中在残差网络的残差连接中加入MHSA。 Neck部分YOLOv8的Neck部分负责特征融合这里添加MSDA可以帮助模型更有效地融合不同层次的特征(yaml文件一和二)。 Backbone可以替换中干网络中的卷积部分 能添加的位置很多一篇文章很难全部介绍到后期我会发文件里面集成上百种的改进机制然后还有许多融合模块给大家。 4.4 MSDA的训练过程截图
下面是添加了MSDA的训练截图。
大家可以看下面的运行结果和添加的位置所以不存在我发的代码不全或者运行不了的问题大家有问题也可以在评论区评论我看到都会为大家解答(我知道的)。 五、本文总结
到此本文的正式分享内容就结束了在这里给大家推荐我的YOLOv8改进有效涨点专栏本专栏目前为新开的平均质量分98分后期我会根据各种最新的前沿顶会进行论文复现也会对一些老的改进机制进行补充目前本专栏免费阅读(暂时大家尽早关注不迷路~)如果大家觉得本文帮助到你了订阅本专栏关注后续更多的更新~ 专栏回顾YOLOv8改进系列专栏——本专栏持续复习各种顶会内容——科研必备
