流媒体网站建设,佛山seo关键词,wordpress 新增选项,网站+建设+拖拉+源码+系统秋招面试专栏推荐 #xff1a;深度学习算法工程师面试问题总结【百面算法工程师】——点击即可跳转 #x1f4a1;#x1f4a1;#x1f4a1;本专栏所有程序均经过测试#xff0c;可成功执行#x1f4a1;#x1f4a1;#x1f4a1; 本文介绍了一种新颖的动态稀疏注意力机制…秋招面试专栏推荐 深度学习算法工程师面试问题总结【百面算法工程师】——点击即可跳转 本专栏所有程序均经过测试可成功执行 本文介绍了一种新颖的动态稀疏注意力机制即通过双层路由来实现更灵活的计算分配并具有内容感知能力。文章在介绍主要的原理后将手把手教学如何进行模块的代码添加和修改并将修改后的完整代码放在文章的最后方便大家一键运行小白也可轻松上手实践。以帮助您更好地学习深度学习目标检测YOLO系列的挑战。 专栏地址YOLO11入门 改进涨点——点击即可跳转 欢迎订阅 目录
1.论文
2. 将BiFormer 添加到YOLO11中
2.1 BiFormer 的代码实现
2.2 更改init.py文件
2.3 添加yaml文件
2.4 在task.py中进行注册
2.5 执行程序
3.修改后的网络结构图
4. 完整代码分享
5. GFLOPs
6. 进阶
7.总结 1.论文 论文地址BiFormer: Vision Transformer with Bi-Level Routing Attention——点击即可跳转 官方代码官方代码仓库——点击即可跳转 2. 将BiFormer 添加到YOLO11中
2.1 BiFormer 的代码实现 关键步骤一: 将下面代码粘贴到在/ultralytics/ultralytics/nn/modules/block.py中 Bi-Level Routing Attention.from typing import Tuple, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch import Tensor, LongTensor__all__ [BiLevelRoutingAttention]class TopkRouting(nn.Module):differentiable topk routing with scalingArgs:qk_dim: int, feature dimension of query and keytopk: int, the topkqk_scale: int or None, temperature (multiply) of softmax activationwith_param: bool, wether inorporate learnable params in routing unitdiff_routing: bool, wether make routing differentiablesoft_routing: bool, wether make output value multiplied by routing weightsdef __init__(self, qk_dim, topk4, qk_scaleNone, param_routingFalse, diff_routingFalse):super().__init__()self.topk topkself.qk_dim qk_dimself.scale qk_scale or qk_dim ** -0.5self.diff_routing diff_routing# TODO: norm layer before/after linear?self.emb nn.Linear(qk_dim, qk_dim) if param_routing else nn.Identity()# routing activationself.routing_act nn.Softmax(dim-1)def forward(self, query: Tensor, key: Tensor) - Tuple[Tensor]:Args:q, k: (n, p^2, c) tensorReturn:r_weight, topk_index: (n, p^2, topk) tensorif not self.diff_routing:query, key query.detach(), key.detach()query_hat, key_hat self.emb(query), self.emb(key) # per-window pooling - (n, p^2, c)attn_logit (query_hat * self.scale) key_hat.transpose(-2, -1) # (n, p^2, p^2)topk_attn_logit, topk_index torch.topk(attn_logit, kself.topk, dim-1) # (n, p^2, k), (n, p^2, k)r_weight self.routing_act(topk_attn_logit) # (n, p^2, k)return r_weight, topk_indexclass KVGather(nn.Module):def __init__(self, mul_weightnone):super().__init__()assert mul_weight in [none, soft, hard]self.mul_weight mul_weightdef forward(self, r_idx: Tensor, r_weight: Tensor, kv: Tensor):r_idx: (n, p^2, topk) tensorr_weight: (n, p^2, topk) tensorkv: (n, p^2, w^2, c_kqc_v)Return:(n, p^2, topk, w^2, c_kqc_v) tensor# select kv according to routing indexn, p2, w2, c_kv kv.size()topk r_idx.size(-1)# print(r_idx.size(), r_weight.size())# FIXME: gather consumes much memory (topk times redundancy), write cuda kernel?topk_kv torch.gather(kv.view(n, 1, p2, w2, c_kv).expand(-1, p2, -1, -1, -1),# (n, p^2, p^2, w^2, c_kv) without mem cpydim2,indexr_idx.view(n, p2, topk, 1, 1).expand(-1, -1, -1, w2, c_kv)# (n, p^2, k, w^2, c_kv))if self.mul_weight soft:topk_kv r_weight.view(n, p2, topk, 1, 1) * topk_kv # (n, p^2, k, w^2, c_kv)elif self.mul_weight hard:raise NotImplementedError(differentiable hard routing TBA)# else: #none# topk_kv topk_kv # do nothingreturn topk_kvclass QKVLinear(nn.Module):def __init__(self, dim, qk_dim, biasTrue):super().__init__()self.dim dimself.qk_dim qk_dimself.qkv nn.Linear(dim, qk_dim qk_dim dim, biasbias)def forward(self, x):q, kv self.qkv(x).split([self.qk_dim, self.qk_dim self.dim], dim-1)return q, kv# q, k, v self.qkv(x).split([self.qk_dim, self.qk_dim, self.dim], dim-1)# return q, k, vclass BiLevelRoutingAttention(nn.Module):n_win: number of windows in one side (so the actual number of windows is n_win*n_win)kv_per_win: for kv_downsample_modeada_xxxpool only, number of key/values per window. Similar to n_win, the actual number is kv_per_win*kv_per_win.topk: topk for window filteringparam_attention: qkvo-linear for q,k,v and o, none: param free attentionparam_routing: extra linear for routingdiff_routing: wether to set routing differentiablesoft_routing: wether to multiply soft routing weightsdef __init__(self, dim, n_win7, num_heads8, qk_dimNone, qk_scaleNone,kv_per_win4, kv_downsample_ratio4, kv_downsample_kernelNone, kv_downsample_modeidentity,topk4, param_attentionqkvo, param_routingFalse, diff_routingFalse, soft_routingFalse,side_dwconv3,auto_padTrue):super().__init__()# local attention settingself.dim dimself.n_win n_win # Wh, Wwself.num_heads num_headsself.qk_dim qk_dim or dimassert self.qk_dim % num_heads 0 and self.dim % num_heads 0, qk_dim and dim must be divisible by num_heads!self.scale qk_scale or self.qk_dim ** -0.5################side_dwconv (i.e. LCE in ShuntedTransformer)###########self.lepe nn.Conv2d(dim, dim, kernel_sizeside_dwconv, stride1, paddingside_dwconv // 2,groupsdim) if side_dwconv 0 else \lambda x: torch.zeros_like(x)################ global routing setting #################self.topk topkself.param_routing param_routingself.diff_routing diff_routingself.soft_routing soft_routing# routerassert not (self.param_routing and not self.diff_routing) # cannot be with_paramTrue and diff_routingFalseself.router TopkRouting(qk_dimself.qk_dim,qk_scaleself.scale,topkself.topk,diff_routingself.diff_routing,param_routingself.param_routing)if self.soft_routing: # soft routing, always diffrentiable (if no detach)mul_weight softelif self.diff_routing: # hard differentiable routingmul_weight hardelse: # hard non-differentiable routingmul_weight noneself.kv_gather KVGather(mul_weightmul_weight)# qkv mapping (shared by both global routing and local attention)self.param_attention param_attentionif self.param_attention qkvo:self.qkv QKVLinear(self.dim, self.qk_dim)self.wo nn.Linear(dim, dim)elif self.param_attention qkv:self.qkv QKVLinear(self.dim, self.qk_dim)self.wo nn.Identity()else:raise ValueError(fparam_attention mode {self.param_attention} is not surpported!)self.kv_downsample_mode kv_downsample_modeself.kv_per_win kv_per_winself.kv_downsample_ratio kv_downsample_ratioself.kv_downsample_kenel kv_downsample_kernelif self.kv_downsample_mode ada_avgpool:assert self.kv_per_win is not Noneself.kv_down nn.AdaptiveAvgPool2d(self.kv_per_win)elif self.kv_downsample_mode ada_maxpool:assert self.kv_per_win is not Noneself.kv_down nn.AdaptiveMaxPool2d(self.kv_per_win)elif self.kv_downsample_mode maxpool:assert self.kv_downsample_ratio is not Noneself.kv_down nn.MaxPool2d(self.kv_downsample_ratio) if self.kv_downsample_ratio 1 else nn.Identity()elif self.kv_downsample_mode avgpool:assert self.kv_downsample_ratio is not Noneself.kv_down nn.AvgPool2d(self.kv_downsample_ratio) if self.kv_downsample_ratio 1 else nn.Identity()elif self.kv_downsample_mode identity: # no kv downsamplingself.kv_down nn.Identity()elif self.kv_downsample_mode fracpool:# assert self.kv_downsample_ratio is not None# assert self.kv_downsample_kenel is not None# TODO: fracpool# 1. kernel size should be input size dependent# 2. there is a random factor, need to avoid independent sampling for k and vraise NotImplementedError(fracpool policy is not implemented yet!)elif kv_downsample_mode conv:# TODO: need to consider the case where k ! v so that need two downsample modulesraise NotImplementedError(conv policy is not implemented yet!)else:raise ValueError(fkv_down_sample_mode {self.kv_downsaple_mode} is not surpported!)# softmax for local attentionself.attn_act nn.Softmax(dim-1)self.auto_pad auto_paddef forward(self, x, ret_attn_maskFalse):x: NHWC tensorReturn:NHWC tensorx rearrange(x, n c h w - n h w c)# NOTE: use padding for semantic segmentation###################################################if self.auto_pad:N, H_in, W_in, C x.size()pad_l pad_t 0pad_r (self.n_win - W_in % self.n_win) % self.n_winpad_b (self.n_win - H_in % self.n_win) % self.n_winx F.pad(x, (0, 0, # dim-1pad_l, pad_r, # dim-2pad_t, pad_b)) # dim-3_, H, W, _ x.size() # padded sizeelse:N, H, W, C x.size()assert H % self.n_win 0 and W % self.n_win 0 ##################################################### patchify, (n, p^2, w, w, c), keep 2d window as we need 2d pooling to reduce kv sizex rearrange(x, n (j h) (i w) c - n (j i) h w c, jself.n_win, iself.n_win)#################qkv projection#################### q: (n, p^2, w, w, c_qk)# kv: (n, p^2, w, w, c_qkc_v)# NOTE: separte kv if there were memory leak issue caused by gatherq, kv self.qkv(x)# pixel-wise qkv# q_pix: (n, p^2, w^2, c_qk)# kv_pix: (n, p^2, h_kv*w_kv, c_qkc_v)q_pix rearrange(q, n p2 h w c - n p2 (h w) c)kv_pix self.kv_down(rearrange(kv, n p2 h w c - (n p2) c h w))kv_pix rearrange(kv_pix, (n j i) c h w - n (j i) (h w) c, jself.n_win, iself.n_win)q_win, k_win q.mean([2, 3]), kv[..., 0:self.qk_dim].mean([2, 3]) # window-wise qk, (n, p^2, c_qk), (n, p^2, c_qk)##################side_dwconv(lepe)################### NOTE: call contiguous to avoid gradient warning when using ddplepe self.lepe(rearrange(kv[..., self.qk_dim:], n (j i) h w c - n c (j h) (i w), jself.n_win,iself.n_win).contiguous())lepe rearrange(lepe, n c (j h) (i w) - n (j h) (i w) c, jself.n_win, iself.n_win)############ gather q dependent k/v #################r_weight, r_idx self.router(q_win, k_win) # both are (n, p^2, topk) tensorskv_pix_sel self.kv_gather(r_idxr_idx, r_weightr_weight, kvkv_pix) # (n, p^2, topk, h_kv*w_kv, c_qkc_v)k_pix_sel, v_pix_sel kv_pix_sel.split([self.qk_dim, self.dim], dim-1)# kv_pix_sel: (n, p^2, topk, h_kv*w_kv, c_qk)# v_pix_sel: (n, p^2, topk, h_kv*w_kv, c_v)######### do attention as normal ####################k_pix_sel rearrange(k_pix_sel, n p2 k w2 (m c) - (n p2) m c (k w2),mself.num_heads) # flatten to BMLC, (n*p^2, m, topk*h_kv*w_kv, c_kq//m) transpose here?v_pix_sel rearrange(v_pix_sel, n p2 k w2 (m c) - (n p2) m (k w2) c,mself.num_heads) # flatten to BMLC, (n*p^2, m, topk*h_kv*w_kv, c_v//m)q_pix rearrange(q_pix, n p2 w2 (m c) - (n p2) m w2 c,mself.num_heads) # to BMLC tensor (n*p^2, m, w^2, c_qk//m)# param-free multihead attentionattn_weight (q_pix * self.scale) k_pix_sel # (n*p^2, m, w^2, c) (n*p^2, m, c, topk*h_kv*w_kv) - (n*p^2, m, w^2, topk*h_kv*w_kv)attn_weight self.attn_act(attn_weight)out attn_weight v_pix_sel # (n*p^2, m, w^2, topk*h_kv*w_kv) (n*p^2, m, topk*h_kv*w_kv, c) - (n*p^2, m, w^2, c)out rearrange(out, (n j i) m (h w) c - n (j h) (i w) (m c), jself.n_win, iself.n_win,hH // self.n_win, wW // self.n_win)out out lepe# output linearout self.wo(out)# NOTE: use padding for semantic segmentation# crop padded regionif self.auto_pad and (pad_r 0 or pad_b 0):out out[:, :H_in, :W_in, :].contiguous()if ret_attn_mask:return out, r_weight, r_idx, attn_weightelse:return rearrange(out, n h w c - n c h w) 2.2 更改init.py文件 关键步骤二修改modules文件夹下的__init__.py文件先导入函数 然后在下面的__all__中声明函数 2.3 添加yaml文件 关键步骤三在/ultralytics/ultralytics/cfg/models/11下面新建文件yolo11_BiFormer.yaml文件粘贴下面的内容 目标检测
# Ultralytics YOLO , AGPL-3.0 license
# YOLO11 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. modelyolo11n.yaml will call yolo11.yaml with scale n# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n 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, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[17, 21, 25], 1, Detect, [nc]] # Detect(P3, P4, P5)语义分割
# Ultralytics YOLO , AGPL-3.0 license
# YOLO11 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. modelyolo11n.yaml will call yolo11.yaml with scale n# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n 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, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[17, 21, 25], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)旋转目标检测
# Ultralytics YOLO , AGPL-3.0 license
# YOLO11 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. modelyolo11n.yaml will call yolo11.yaml with scale n# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n 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, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 1, BiLevelRoutingAttention, []]- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[17, 21, 25], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)温馨提示本文只是对yolo11基础上添加模块如果要对yolo11n/l/m/x进行添加则只需要指定对应的depth_multiple 和 width_multiple。 # YOLO11n
depth_multiple: 0.50 # model depth multiple
width_multiple: 0.25 # layer channel multiple
max_channel1024# YOLO11s
depth_multiple: 0.50 # model depth multiple
width_multiple: 0.50 # layer channel multiple
max_channel1024# YOLO11m
depth_multiple: 0.50 # model depth multiple
width_multiple: 1.00 # layer channel multiple
max_channel512# YOLO11l
depth_multiple: 1.00 # model depth multiple
width_multiple: 1.00 # layer channel multiple
max_channel512 # YOLO11x
depth_multiple: 1.00 # model depth multiple
width_multiple: 1.50 # layer channel multiple
max_channel512
2.4 在task.py中进行注册 关键步骤四在task.py的parse_model函数中进行注册 先在task.py导入函数 然后在task.py文件下找到parse_model这个函数如下图添加BiLevelRoutingAttention elif m in {BiLevelRoutingAttention}:c2 ch[f]args [c2, *args]
2.5 执行程序 关键步骤五在ultralytics文件中新建train.py将model的参数路径设置为yolo11_BiFormer.yaml的路径即可 from ultralytics import YOLO
import warnings
warnings.filterwarnings(ignore)
from pathlib import Pathif __name__ __main__:# 加载模型model YOLO(ultralytics/cfg/11/yolo11.yaml) # 你要选择的模型yaml文件地址# Use the modelresults model.train(datar你的数据集的yaml文件地址,epochs100, batch16, imgsz640, workers4, namePath(model.cfg).stem) # 训练模型 运行程序如果出现下面的内容则说明添加成功 from n params module arguments0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True]7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True]9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1]11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest]12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False]14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest]15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]16 -1 1 265728 ultralytics.nn.modules.block.BiLevelRoutingAttention[256]17 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False]18 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]19 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]20 -1 1 150144 ultralytics.nn.modules.block.BiLevelRoutingAttention[192]21 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False]22 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]23 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]24 -1 1 595200 ultralytics.nn.modules.block.BiLevelRoutingAttention[384]25 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True]26 [17, 21, 25] 1 464912 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]]
YOLO11_Biformer summary: 352 layers, 3,635,152 parameters, 3,635,136 gradients, 46.1 GFLOPs
3.修改后的网络结构图
看不懂的可以问我偷个懒 4. 完整代码分享
这个后期补充吧~先按照步骤来即可
5. GFLOPs
关于GFLOPs的计算方式可以查看百面算法工程师 | 卷积基础知识——Convolution
未改进的YOLO11n GFLOPs 改进后的GFLOPs 6. 进阶
可以与其他的注意力机制或者损失函数等结合进一步提升检测效果
7.总结
通过以上的改进方法我们成功提升了模型的表现。这只是一个开始未来还有更多优化和技术深挖的空间。在这里我想隆重向大家推荐我的专栏——《YOLO11改进有效涨点》。这个专栏专注于前沿的深度学习技术特别是目标检测领域的最新进展不仅包含对YOLO11的深入解析和改进策略还会定期更新来自各大顶会如CVPR、NeurIPS等的论文复现和实战分享。
为什么订阅我的专栏 ——《YOLO11改进有效涨点》 前沿技术解读专栏不仅限于YOLO系列的改进还会涵盖各类主流与新兴网络的最新研究成果帮助你紧跟技术潮流。 详尽的实践分享所有内容实践性也极强。每次更新都会附带代码和具体的改进步骤保证每位读者都能迅速上手。 问题互动与答疑订阅我的专栏后你将可以随时向我提问获取及时的答疑。 实时更新紧跟行业动态不定期发布来自全球顶会的最新研究方向和复现实验报告让你时刻走在技术前沿。
专栏适合人群 对目标检测、YOLO系列网络有深厚兴趣的同学 希望在用YOLO算法写论文的同学 对YOLO算法感兴趣的同学等
