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💡💡💡
本文给大家带来的教程是将YOLO11的backbone替换为SimRepCSP结构来提取特征。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：YOLO11入门 + 改进涨点——点击即可跳转 欢迎订阅
目录
1.论文
2. SimRepCSP代码实现
2.1 将SimRepCSP添加到YOLO11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. GFLOPs
6. 进阶
7.总结
1.论文
官方论文：Modified YOLO Model for Small Platform Application using SimRepCSP Module with Case Study——点击即可跳转
2. SimRepCSP代码实现
2.1 将SimRepCSP添加到YOLO11中
关键步骤一：将下面代码粘贴到在/ultralytics/ultralytics/nn/modules/block.py中
class RepConv(nn.Module):# Represented convolution# https://arxiv.org/abs/2101.03697def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False):super(RepConv, self).__init__()self.deploy = deployself.groups = gself.in_channels = c1self.out_channels = c2assert k == 3assert autopad(k, p) == 1padding_11 = autopad(k, p) - k // 2self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) #if deploy:self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True)else:self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None)self.rbr_dense = nn.Sequential(nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False),nn.BatchNorm2d(num_features=c2),)self.rbr_1x1 = nn.Sequential(nn.Conv2d( c1, c2, 1, s, padding_11, groups=g, bias=False),nn.BatchNorm2d(num_features=c2),)def forward(self, inputs):if hasattr(self, "rbr_reparam"):return self.act(self.rbr_reparam(inputs))if self.rbr_identity is None:id_out = 0else:id_out = self.rbr_identity(inputs)return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)def get_equivalent_kernel_bias(self):kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)return (kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid,bias3x3 + bias1x1 + biasid,)def _pad_1x1_to_3x3_tensor(self, kernel1x1):if kernel1x1 is None:return 0else:return nn.functional.pad(kernel1x1, [1, 1, 1, 1])def _fuse_bn_tensor(self, branch):if branch is None:return 0, 0if isinstance(branch, nn.Sequential):kernel = branch[0].weightrunning_mean = branch[1].running_meanrunning_var = branch[1].running_vargamma = branch[1].weightbeta = branch[1].biaseps = branch[1].epselse:assert isinstance(branch, nn.BatchNorm2d)if not hasattr(self, "id_tensor"):input_dim = self.in_channels // self.groupskernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)for i in range(self.in_channels):kernel_value[i, i % input_dim, 1, 1] = 1self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)kernel = self.id_tensorrunning_mean = branch.running_meanrunning_var = branch.running_vargamma = branch.weightbeta = branch.biaseps = branch.epsstd = (running_var + eps).sqrt()t = (gamma / std).reshape(-1, 1, 1, 1)return kernel * t, beta - running_mean * gamma / stddef repvgg_convert(self):kernel, bias = self.get_equivalent_kernel_bias()return (kernel.detach().cpu().numpy(),bias.detach().cpu().numpy(),)def fuse_conv_bn(self, conv, bn):std = (bn.running_var + bn.eps).sqrt()bias = bn.bias - bn.running_mean * bn.weight / stdt = (bn.weight / std).reshape(-1, 1, 1, 1)weights = conv.weight * tbn = nn.Identity()conv = nn.Conv2d(in_channels = conv.in_channels,out_channels = conv.out_channels,kernel_size = conv.kernel_size,stride=conv.stride,padding = conv.padding,dilation = conv.dilation,groups = conv.groups,bias = True,padding_mode = conv.padding_mode)conv.weight = torch.nn.Parameter(weights)conv.bias = torch.nn.Parameter(bias)return convdef fuse_repvgg_block(self): if self.deploy:returnprint(f"RepConv.fuse_repvgg_block")self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1])self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1])rbr_1x1_bias = self.rbr_1x1.biasweight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1])# Fuse self.rbr_identityif (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)):# print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm")identity_conv_1x1 = nn.Conv2d(in_channels=self.in_channels,out_channels=self.out_channels,kernel_size=1,stride=1,padding=0,groups=self.groups, bias=False)identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device)identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze()# print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")identity_conv_1x1.weight.data.fill_(0.0)identity_conv_1x1.weight.data.fill_diagonal_(1.0)identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3)# print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity)bias_identity_expanded = identity_conv_1x1.biasweight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1]) else:# print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}")bias_identity_expanded = torch.nn.Parameter( torch.zeros_like(rbr_1x1_bias) )weight_identity_expanded = torch.nn.Parameter( torch.zeros_like(weight_1x1_expanded) ) #print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ")#print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ")#print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ")self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded)self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded)self.rbr_reparam = self.rbr_denseself.deploy = Trueif self.rbr_identity is not None:del self.rbr_identityself.rbr_identity = Noneif self.rbr_1x1 is not None:del self.rbr_1x1self.rbr_1x1 = Noneif self.rbr_dense is not None:del self.rbr_denseself.rbr_dense = None2.2 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_SimRepCSP.yaml文件，粘贴下面的内容
- 目标检测
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model. More improvement points for YOLOv8, please see https://github.com/iscyy/ultralyticsPro# 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 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, 1, Conv, [64, 1, 1]]- [-1, 1, RepConv, [64, 3, 1]]- [[-1,-2], 1, Concat, [1]] #4- [-1, 1, Conv, [128, 1, 1]]- [-1, 1, Conv, [256, 3, 2]]- [-1, 1, Conv, [128, 1, 1]] #7- [-1, 1, RepConv, [128, 3, 1]] #8- [[-1,-2], 1, Concat, [1]] #9- [-1, 1, Conv, [256, 1, 1]] #10 -P4/16 - [-1, 1, Conv, [512, 3, 2]] #11- [-1, 1, Conv, [256, 1, 1]] #12- [-1, 1, RepConv, [256, 3, 1]] #13- [[-1,-2], 1, Concat, [1]] #14- [-1, 1, Conv, [512, 1, 1]] #15- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 16- [-1, 1, Conv, [512, 1, 1]] #17- [-1, 1, RepConv, [512, 3, 1]] #18- [[-1,-2], 1, Concat, [1]] #19- [-1, 1, Conv, [1024, 1, 1]] #20- [-1, 1, SPPF, [1024, 5]] # 21# YOLOv8.0n head
head:- [-1, 1, Conv, [512, 1, 1]] #22- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 11], 1, Concat, [1]] # cat backbone P4- [-1, 3, C3k2, [512, False]] # 25- [-1, 1, Conv, [256, 1, 1]] #26- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]] #28 cat backbone P3- [-1, 3, C3k2, [256, False]] # 29 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1,26], 1, Concat, [1]] # 31 cat head P4- [-1, 3, C3k2, [512, False]] # 32 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 22], 1, Concat, [1]] #34 cat head P5- [-1, 3, C3k2, [1024, False]] #35 (P5/32-large)- [[29, 32, 35], 1, Detect, [nc]] # Detect(P3, P4, P5)- 语义分割
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model. More improvement points for YOLOv8, please see https://github.com/iscyy/ultralyticsPro# 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 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, 1, Conv, [64, 1, 1]]- [-1, 1, RepConv, [64, 3, 1]]- [[-1,-2], 1, Concat, [1]] #4- [-1, 1, Conv, [128, 1, 1]]- [-1, 1, Conv, [256, 3, 2]]- [-1, 1, Conv, [128, 1, 1]] #7- [-1, 1, RepConv, [128, 3, 1]] #8- [[-1,-2], 1, Concat, [1]] #9- [-1, 1, Conv, [256, 1, 1]] #10 -P4/16 - [-1, 1, Conv, [512, 3, 2]] #11- [-1, 1, Conv, [256, 1, 1]] #12- [-1, 1, RepConv, [256, 3, 1]] #13- [[-1,-2], 1, Concat, [1]] #14- [-1, 1, Conv, [512, 1, 1]] #15- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 16- [-1, 1, Conv, [512, 1, 1]] #17- [-1, 1, RepConv, [512, 3, 1]] #18- [[-1,-2], 1, Concat, [1]] #19- [-1, 1, Conv, [1024, 1, 1]] #20- [-1, 1, SPPF, [1024, 5]] # 21# YOLOv8.0n head
head:- [-1, 1, Conv, [512, 1, 1]] #22- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 11], 1, Concat, [1]] # cat backbone P4- [-1, 3, C3k2, [512, False]] # 25- [-1, 1, Conv, [256, 1, 1]] #26- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]] #28 cat backbone P3- [-1, 3, C3k2, [256, False]] # 29 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1,26], 1, Concat, [1]] # 31 cat head P4- [-1, 3, C3k2, [512, False]] # 32 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 22], 1, Concat, [1]] #34 cat head P5- [-1, 3, C3k2, [1024, False]] #35 (P5/32-large)- [[29, 32, 35], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)- 旋转目标检测
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model. More improvement points for YOLOv8, please see https://github.com/iscyy/ultralyticsPro# 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 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, 1, Conv, [64, 1, 1]]- [-1, 1, RepConv, [64, 3, 1]]- [[-1,-2], 1, Concat, [1]] #4- [-1, 1, Conv, [128, 1, 1]]- [-1, 1, Conv, [256, 3, 2]]- [-1, 1, Conv, [128, 1, 1]] #7- [-1, 1, RepConv, [128, 3, 1]] #8- [[-1,-2], 1, Concat, [1]] #9- [-1, 1, Conv, [256, 1, 1]] #10 -P4/16 - [-1, 1, Conv, [512, 3, 2]] #11- [-1, 1, Conv, [256, 1, 1]] #12- [-1, 1, RepConv, [256, 3, 1]] #13- [[-1,-2], 1, Concat, [1]] #14- [-1, 1, Conv, [512, 1, 1]] #15- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 16- [-1, 1, Conv, [512, 1, 1]] #17- [-1, 1, RepConv, [512, 3, 1]] #18- [[-1,-2], 1, Concat, [1]] #19- [-1, 1, Conv, [1024, 1, 1]] #20- [-1, 1, SPPF, [1024, 5]] # 21# YOLOv8.0n head
head:- [-1, 1, Conv, [512, 1, 1]] #22- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 11], 1, Concat, [1]] # cat backbone P4- [-1, 3, C3k2, [512, False]] # 25- [-1, 1, Conv, [256, 1, 1]] #26- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]] #28 cat backbone P3- [-1, 3, C3k2, [256, False]] # 29 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1,26], 1, Concat, [1]] # 31 cat head P4- [-1, 3, C3k2, [512, False]] # 32 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 22], 1, Concat, [1]] #34 cat head P5- [-1, 3, C3k2, [1024, False]] #35 (P5/32-large)- [[29, 32, 35], 1, OBB, [nc, 1]] # OBB(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_channel：1024# YOLO11s
depth_multiple: 0.50 # model depth multiple
width_multiple: 0.50 # layer channel multiple
max_channel：1024# YOLO11m
depth_multiple: 0.50 # model depth multiple
width_multiple: 1.00 # layer channel multiple
max_channel：512# YOLO11l
depth_multiple: 1.00 # model depth multiple
width_multiple: 1.00 # layer channel multiple
max_channel：512 # YOLO11x
depth_multiple: 1.00 # model depth multiple
width_multiple: 1.50 # layer channel multiple
max_channel：5122.4 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GCNet
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GCNet
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_SimRepCSP.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(data=r"你的数据集的yaml文件地址",epochs=100, batch=16, imgsz=640, workers=4, name=Path(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 1 544 ultralytics.nn.modules.conv.Conv [32, 16, 1, 1]3 -1 1 2624 ultralytics.nn.modules.conv.RepConv [16, 16, 3, 1]4 [-1, -2] 1 0 ultralytics.nn.modules.conv.Concat [1]5 -1 1 1088 ultralytics.nn.modules.conv.Conv [32, 32, 1, 1]6 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]7 -1 1 2112 ultralytics.nn.modules.conv.Conv [64, 32, 1, 1]8 -1 1 10368 ultralytics.nn.modules.conv.RepConv [32, 32, 3, 1]9 [-1, -2] 1 0 ultralytics.nn.modules.conv.Concat [1]10 -1 1 4224 ultralytics.nn.modules.conv.Conv [64, 64, 1, 1]11 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]12 -1 1 8320 ultralytics.nn.modules.conv.Conv [128, 64, 1, 1]13 -1 1 41216 ultralytics.nn.modules.conv.RepConv [64, 64, 3, 1]14 [-1, -2] 1 0 ultralytics.nn.modules.conv.Concat [1]15 -1 1 16640 ultralytics.nn.modules.conv.Conv [128, 128, 1, 1]16 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]17 -1 1 33024 ultralytics.nn.modules.conv.Conv [256, 128, 1, 1]18 -1 1 164352 ultralytics.nn.modules.conv.RepConv [128, 128, 3, 1]19 [-1, -2] 1 0 ultralytics.nn.modules.conv.Concat [1]20 -1 1 66048 ultralytics.nn.modules.conv.Conv [256, 256, 1, 1]21 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]22 -1 1 33024 ultralytics.nn.modules.conv.Conv [256, 128, 1, 1]23 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']24 [-1, 11] 1 0 ultralytics.nn.modules.conv.Concat [1]25 -1 1 94912 ultralytics.nn.modules.block.C3k2 [256, 128, 1, False]26 -1 1 8320 ultralytics.nn.modules.conv.Conv [128, 64, 1, 1]27 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']28 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]29 -1 1 23904 ultralytics.nn.modules.block.C3k2 [128, 64, 1, False]30 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]31 [-1, 26] 1 0 ultralytics.nn.modules.conv.Concat [1]32 -1 1 78528 ultralytics.nn.modules.block.C3k2 [128, 128, 1, False]33 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]34 [-1, 22] 1 0 ultralytics.nn.modules.conv.Concat [1]35 -1 1 312704 ultralytics.nn.modules.block.C3k2 [256, 256, 1, False]36 [29, 32, 35] 1 464912 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]]
YOLO11_SimRepCSP summary: 246 layers, 2,109,280 parameters, 2,109,264 gradients, 5.4 GFLOPs3. 完整代码分享
这个后期补充吧~，先按照步骤来即可
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——<专栏地址：YOLO11入门 + 改进涨点——点击即可跳转 欢迎订阅>。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？ ——专栏地址：YOLO11入门 + 改进涨点——点击即可跳转 欢迎订阅
-
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
_谷歌seo软件_互联网广告平台有哪些 "邯郸最新消息_政府招标网(免费)_谷歌seo软件_互联网广告平台有哪些")