本专栏所有程序均经过测试可成功执行本文给大家带来的教程是将YOLO26的下采样替换为DRFD来提取特征。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YOLO26改进-论文涨点——点击跳转看所有内容关注不迷路目录1.论文2. Conv_BCN代码实现2.1 将Conv_BCN添加到YOLO26中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. 完整代码分享4. GFLOPs5. 进阶6.总结1.论文论文地址A Robust Feature Downsampling Module for Remote-Sensing Visual Tasks官方代码官方代码仓库点击即可跳转2. DRFD代码实现2.1 将DRFD添加到YOLO26中关键步骤一在ultralytics\ultralytics\nn\modules下面新建文件夹models在文件夹下新建DRFD.py粘贴下面代码import torch import torch.nn as nn class Cut(nn.Module): def __init__(self, in_channels, out_channels): super().__init__() self.conv_fusion nn.Conv2d(in_channels * 4, out_channels, kernel_size1, stride1) self.batch_norm nn.BatchNorm2d(out_channels) def forward(self, x): x0 x[:, :, 0::2, 0::2] # x [B, C, H/2, W/2] x1 x[:, :, 1::2, 0::2] x2 x[:, :, 0::2, 1::2] x3 x[:, :, 1::2, 1::2] x torch.cat([x0, x1, x2, x3], dim1) # x [B, 4*C, H/2, W/2] x self.conv_fusion(x) # x [B, out_channels, H/2, W/2] x self.batch_norm(x) return x class DRFD(nn.Module): def __init__(self, in_channels, out_channels): super().__init__() self.cut_c Cut(in_channelsin_channels, out_channelsout_channels) self.conv nn.Conv2d(in_channels, out_channels, kernel_size3, stride1, padding1, groupsin_channels) self.conv_x nn.Conv2d(out_channels, out_channels, kernel_size3, stride2, padding1, groupsout_channels) self.act_x nn.GELU() self.batch_norm_x nn.BatchNorm2d(out_channels) self.batch_norm_m nn.BatchNorm2d(out_channels) self.max_m nn.MaxPool2d(kernel_size2, stride2) self.fusion nn.Conv2d(3 * out_channels, out_channels, kernel_size1, stride1) def forward(self, x): # input: x [B, C, H, W] c x # c [B, C, H, W] x self.conv(x) # x [B, C, H, W] -- [B, 2C, H, W] m x # m [B, 2C, H, W] # CutD c self.cut_c(c) # c [B, C, H, W] -- [B, 2C, H/2, W/2] # ConvD x self.conv_x(x) # x [B, 2C, H, W] -- [B, 2C, H/2, W/2] x self.act_x(x) x self.batch_norm_x(x) # MaxD m self.max_m(m) # m [B, 2C, H/2, W/2] m self.batch_norm_m(m) # Concat conv x torch.cat([c, x, m], dim1) # x [B, 6C, H/2, W/2] x self.fusion(x) # x [B, 6C, H/2, W/2] -- [B, 2C, H/2, W/2] return x # x [B, 2C, H/2, W/2]2.2 更改init.py文件关键步骤二在文件ultralytics\ultralytics\nn\modules\models文件夹下新建__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/26下面新建文件yolo26_DRFD.yaml文件粘贴下面的内容目标检测# Ultralytics AGPL-3.0 License - https://ultralytics.com/license # Ultralytics YOLO26 object detection model with P3/8 - P5/32 outputs # Model docs: https://docs.ultralytics.com/models/yolo26 # Task docs: https://docs.ultralytics.com/tasks/detect # Parameters nc: 80 # number of classes end2end: True # whether to use end-to-end mode reg_max: 1 # DFL bins scales: # model compound scaling constants, i.e. modelyolo26n.yaml will call yolo26.yaml with scale n # [depth, width, max_channels] n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs # YOLO26n 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]] # 2-P2/4 - [-1, 1, DRFD, [256]] # 3-P3/8 - [-1, 2, C3k2, [512, False, 0.25]] # 4-P3/8 - [-1, 1, DRFD, [512]] # 5-P4/16 - [-1, 2, C3k2, [512, True]] # 6-P4/16 - [-1, 1, DRFD, [1024]] # 7-P5/32 - [-1, 2, C3k2, [1024, True]] # 8-P5/32 - [-1, 1, SPPF, [1024, 5, 3, True]] # 9-P5/32 - [-1, 2, C2PSA, [1024]] # 10-P5/32 # YOLO26n head head: - [-1, 1, nn.Upsample, [None, 2, nearest]] # 11-P4/16 - [[-1, 6], 1, Concat, [1]] # 12-P4/16 - [-1, 2, C3k2, [512, True]] # 13-P4/16 - [-1, 1, nn.Upsample, [None, 2, nearest]] # 14-P3/8 - [[-1, 4], 1, Concat, [1]] # 15-P3/8 - [-1, 2, C3k2, [256, True]] # 16-P3/8 - [-1, 1, DRFD, [256]] # 17-P4/16 - [[-1, 13], 1, Concat, [1]] # 18-P4/16 - [-1, 2, C3k2, [512, True]] # 19-P4/16 - [-1, 1, DRFD, [512]] # 20-P5/32 - [[-1, 10], 1, Concat, [1]] # 21-P5/32 - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22-P5/32 - [[16, 19, 22], 1, Detect, [nc]] # 23-P3/8,P4/16,P5/32语义分割# Ultralytics AGPL-3.0 License - https://ultralytics.com/license # Ultralytics YOLO26 object detection model with P3/8 - P5/32 outputs # Model docs: https://docs.ultralytics.com/models/yolo26 # Task docs: https://docs.ultralytics.com/tasks/detect # Parameters nc: 80 # number of classes end2end: True # whether to use end-to-end mode reg_max: 1 # DFL bins scales: # model compound scaling constants, i.e. modelyolo26n.yaml will call yolo26.yaml with scale n # [depth, width, max_channels] n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs # YOLO26n 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]] # 2-P2/4 - [-1, 1, DRFD, [256]] # 3-P3/8 - [-1, 2, C3k2, [512, False, 0.25]] # 4-P3/8 - [-1, 1, DRFD, [512]] # 5-P4/16 - [-1, 2, C3k2, [512, True]] # 6-P4/16 - [-1, 1, DRFD, [1024]] # 7-P5/32 - [-1, 2, C3k2, [1024, True]] # 8-P5/32 - [-1, 1, SPPF, [1024, 5, 3, True]] # 9-P5/32 - [-1, 2, C2PSA, [1024]] # 10-P5/32 # YOLO26n head head: - [-1, 1, nn.Upsample, [None, 2, nearest]] # 11-P4/16 - [[-1, 6], 1, Concat, [1]] # 12-P4/16 - [-1, 2, C3k2, [512, True]] # 13-P4/16 - [-1, 1, nn.Upsample, [None, 2, nearest]] # 14-P3/8 - [[-1, 4], 1, Concat, [1]] # 15-P3/8 - [-1, 2, C3k2, [256, True]] # 16-P3/8 - [-1, 1, DRFD, [256]] # 17-P4/16 - [[-1, 13], 1, Concat, [1]] # 18-P4/16 - [-1, 2, C3k2, [512, True]] # 19-P4/16 - [-1, 1, DRFD, [512]] # 20-P5/32 - [[-1, 10], 1, Concat, [1]] # 21-P5/32 - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22-P5/32 - [[16, 19, 22], 1, Segment, [nc, 32, 256]]旋转目标检测# Ultralytics AGPL-3.0 License - https://ultralytics.com/license # Ultralytics YOLO26 object detection model with P3/8 - P5/32 outputs # Model docs: https://docs.ultralytics.com/models/yolo26 # Task docs: https://docs.ultralytics.com/tasks/detect # Parameters nc: 80 # number of classes end2end: True # whether to use end-to-end mode reg_max: 1 # DFL bins scales: # model compound scaling constants, i.e. modelyolo26n.yaml will call yolo26.yaml with scale n # [depth, width, max_channels] n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs # YOLO26n 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]] # 2-P2/4 - [-1, 1, DRFD, [256]] # 3-P3/8 - [-1, 2, C3k2, [512, False, 0.25]] # 4-P3/8 - [-1, 1, DRFD, [512]] # 5-P4/16 - [-1, 2, C3k2, [512, True]] # 6-P4/16 - [-1, 1, DRFD, [1024]] # 7-P5/32 - [-1, 2, C3k2, [1024, True]] # 8-P5/32 - [-1, 1, SPPF, [1024, 5, 3, True]] # 9-P5/32 - [-1, 2, C2PSA, [1024]] # 10-P5/32 # YOLO26n head head: - [-1, 1, nn.Upsample, [None, 2, nearest]] # 11-P4/16 - [[-1, 6], 1, Concat, [1]] # 12-P4/16 - [-1, 2, C3k2, [512, True]] # 13-P4/16 - [-1, 1, nn.Upsample, [None, 2, nearest]] # 14-P3/8 - [[-1, 4], 1, Concat, [1]] # 15-P3/8 - [-1, 2, C3k2, [256, True]] # 16-P3/8 - [-1, 1, DRFD, [256]] # 17-P4/16 - [[-1, 13], 1, Concat, [1]] # 18-P4/16 - [-1, 2, C3k2, [512, True]] # 19-P4/16 - [-1, 1, DRFD, [512]] # 20-P5/32 - [[-1, 10], 1, Concat, [1]] # 21-P5/32 - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22-P5/32 - [[16, 19, 22], 1, OBB, [nc, 1]]温馨提示本文只是对yolo26基础上添加模块如果要对yolo26 n/l/m/x进行添加则只需要指定对应的depth_multiple 和 width_multipleend2end: True # whether to use end-to-end mode reg_max: 1 # DFL bins scales: # model compound scaling constants, i.e. modelyolo26n.yaml will call yolo26.yaml with scale n # [depth, width, max_channels] n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs2.4 在task.py中进行注册关键步骤四在parse_model函数中进行注册添加DRFD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DRFDelif m in {DRFD}: c1, c2 ch[f], args[0] if c2 ! nc: # if c2 ! nc (e.g., Classify() output) c2 make_divisible(min(c2, max_channels) * width, 8) args [c1, c2, *args[1:]]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yolo26_DRFD.yaml的路径即可 【注意是在外边的Ultralytics下新建train.py】from ultralytics import YOLO import warnings warnings.filterwarnings(ignore) from pathlib import Path if __name__ __main__: # 加载模型 model YOLO(ultralytics/cfg/26/yolo26.yaml) # 你要选择的模型yaml文件地址 # Use the model results 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 arguments 0 -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 30464 ultralytics.nn.models.DRFD.DRFD [64, 64] 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] 5 -1 1 118272 ultralytics.nn.models.DRFD.DRFD [128, 128] 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] 7 -1 1 334848 ultralytics.nn.models.DRFD.DRFD [128, 256] 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, 3, True] 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 119808 ultralytics.nn.modules.block.C3k2 [384, 128, 1, True] 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 34304 ultralytics.nn.modules.block.C3k2 [256, 64, 1, True] 17 -1 1 30464 ultralytics.nn.models.DRFD.DRFD [64, 64] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 95232 ultralytics.nn.modules.block.C3k2 [192, 128, 1, True] 20 -1 1 118272 ultralytics.nn.models.DRFD.DRFD [128, 128] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 463104 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True, 0.5, True] 23 [16, 19, 22] 1 309656 ultralytics.nn.modules.head.Detect [80, 1, True, [64, 128, 256]] YOLO26_DRFD summary: 295 layers, 2,539,768 parameters, 2,539,768 gradients, 6.0 GFLOPs3. 完整代码分享主页侧边4. 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YOLO26n GFLOPs​改进后的GFLOPs5. 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6.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——专栏地址YOLO26改进-论文涨点——点击跳转看所有内容关注不迷路。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YOLO26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——专栏地址YOLO26改进-论文涨点——点击跳转看所有内容关注不迷路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​