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前言

<code>EfficientViT是一种新的高分辨率视觉模型家族，具有新颖的多尺度线性注意机制。本文详细介绍了如何使用efficientViT网络替换YOLOV8的主干网络结构，并且使用修改后的yolov8进行目标检测训练与推理。本文提供了所有源码免费供小伙伴们学习参考，需要的可以通过文末方式自行下载。

本文使用的ultralytics版本为：ultralytics == 8.0.227。

目录

前言1. efficientViT简介1.1 efficientViT网络结构1.2 性能对比

2.使用efficientViT替换YOLOV8主干网络结构第1步--添加efficientVit.py文件，并导入第2步--修改tasks.py中的相关内容parse_model函数修改parse_model修改的详细内容对比_predict_once函数修改

第3步：创建配置文件--yolov8-efficientViT.yaml`yolov8.yaml`与`yolov8-efficientViT.yaml`对比

第4步：加载配置文件训练模型第5步：模型推理

【源码获取】结束语

1. efficientViT简介

论文发表时间：2023.09.27

github地址：https://github.com/mit-han-lab/efficientvit

paper地址：https://arxiv.org/abs/2205.14756

<code>摘要：高分辨率密集预测技术能够实现许多吸引人的实际应用，比如计算摄影、自动驾驶等。然而，巨大的计算成本使得在硬件设备上部署最先进的高分辨率密集预测模型变得困难。本研究提出了EfficientViT，一种新的高分辨率视觉模型家族，具有新颖的多尺度线性注意机制。与先前依赖于重型softmax注意力、硬件效率低下的大卷积核卷积或复杂的拓扑结构来获得良好性能的高分辨率密集预测模型不同，我们的多尺度线性注意力通过轻量级而且硬件高效的操作实现了全局感受野和多尺度学习（这对高分辨率密集预测是两个理想的特性）。因此，EfficientViT在各种硬件平台上实现了显著的性能提升，并且具有显著的加速能力，包括移动CPU、边缘GPU等。

论文亮点如下:

• 我们引入了一种新的多尺度线性注意力模块，用于高效的高分辨率密集预测。它在保持硬件效率的同时实现了全局感知域和多尺度学习。据我们所知，我们的工作是首次展示线性注意力对于高分辨率密集预测的有效性。

• 我们基于提出的多尺度线性注意力模块设计了一种新型的高分辨率视觉模型——EfficientViT。

• 我们的模型在语义分割、超分辨率、任意分割和ImageNet分类等各种硬件平台（移动CPU、边缘GPU和云GPU）上相对于先前的SOTA模型展现出了显著的加速效果。

1.1 efficientViT网络结构

1.2 性能对比

2.使用efficientViT替换YOLOV8主干网络结构

首先，在yolov8官网下载代码并解压，地址如下：

https://github.com/ultralytics/ultralytics

解压后，如下图所示：

第1步–添加efficientVit.py文件，并导入

在<code>ultralytics/nn/backbone目录下，新建backbone网络文件efficientVit.py，内容如下：

<code>import torch

import torch.nn as nn

import torch.nn.functional as F

import torch.utils.checkpoint as checkpoint

import itertools

from timm.models.layers import SqueezeExcite

import numpy as np

import itertools

__all__ = ['EfficientViT_M0', 'EfficientViT_M1', 'EfficientViT_M2', 'EfficientViT_M3', 'EfficientViT_M4', 'EfficientViT_M5']

class Conv2d_BN(torch.nn.Sequential):

def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,

groups=1, bn_weight_init=1, resolution=-10000):

super().__init__()

self.add_module('c', torch.nn.Conv2d(

a, b, ks, stride, pad, dilation, groups, bias=False))

self.add_module('bn', torch.nn.BatchNorm2d(b))

torch.nn.init.constant_(self.bn.weight, bn_weight_init)

torch.nn.init.constant_(self.bn.bias, 0)

@torch.no_grad()

def switch_to_deploy(self):

c, bn = self._modules.values()

w = bn.weight / (bn.running_var + bn.eps)**0.5

w = c.weight * w[:, None, None, None]

b = bn.bias - bn.running_mean * bn.weight / \

(bn.running_var + bn.eps)**0.5

m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(

0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)

m.weight.data.copy_(w)

m.bias.data.copy_(b)

return m

def replace_batchnorm(net):

for child_name, child in net.named_children():

if hasattr(child, 'fuse'):

setattr(net, child_name, child.fuse())

elif isinstance(child, torch.nn.BatchNorm2d):

setattr(net, child_name, torch.nn.Identity())

else:

replace_batchnorm(child)

class PatchMerging(torch.nn.Module):

def __init__(self, dim, out_dim, input_resolution):

super().__init__()

hid_dim = int(dim * 4)

self.conv1 = Conv2d_BN(dim, hid_dim, 1, 1, 0, resolution=input_resolution)

self.act = torch.nn.ReLU()

self.conv2 = Conv2d_BN(hid_dim, hid_dim, 3, 2, 1, groups=hid_dim, resolution=input_resolution)

self.se = SqueezeExcite(hid_dim, .25)

self.conv3 = Conv2d_BN(hid_dim, out_dim, 1, 1, 0, resolution=input_resolution // 2)

def forward(self, x):

x = self.conv3(self.se(self.act(self.conv2(self.act(self.conv1(x))))))

return x

class Residual(torch.nn.Module):

def __init__(self, m, drop=0.):

super().__init__()

self.m = m

self.drop = drop

def forward(self, x):

if self.training and self.drop > 0:

return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,

device=x.device).ge_(self.drop).div(1 - self.drop).detach()

else:

return x + self.m(x)

class FFN(torch.nn.Module):

def __init__(self, ed, h, resolution):

super().__init__()

self.pw1 = Conv2d_BN(ed, h, resolution=resolution)

self.act = torch.nn.ReLU()

self.pw2 = Conv2d_BN(h, ed, bn_weight_init=0, resolution=resolution)

def forward(self, x):

x = self.pw2(self.act(self.pw1(x)))

return x

class CascadedGroupAttention(torch.nn.Module):

r""" Cascaded Group Attention.

Args:

dim (int): Number of input channels.

key_dim (int): The dimension for query and key.

num_heads (int): Number of attention heads.

attn_ratio (int): Multiplier for the query dim for value dimension.

resolution (int): Input resolution, correspond to the window size.

kernels (List[int]): The kernel size of the dw conv on query.

"""

def __init__(self, dim, key_dim, num_heads=8,

attn_ratio=4,

resolution=14,

kernels=[5, 5, 5, 5],):

super().__init__()

self.num_heads = num_heads

self.scale = key_dim ** -0.5

self.key_dim = key_dim

self.d = int(attn_ratio * key_dim)

self.attn_ratio = attn_ratio

qkvs = []

dws = []

for i in range(num_heads):

qkvs.append(Conv2d_BN(dim // (num_heads), self.key_dim * 2 + self.d, resolution=resolution))

dws.append(Conv2d_BN(self.key_dim, self.key_dim, kernels[i], 1, kernels[i]//2, groups=self.key_dim, resolution=resolution))

self.qkvs = torch.nn.ModuleList(qkvs)

self.dws = torch.nn.ModuleList(dws)

self.proj = torch.nn.Sequential(torch.nn.ReLU(), Conv2d_BN(

self.d * num_heads, dim, bn_weight_init=0, resolution=resolution))

points = list(itertools.product(range(resolution), range(resolution)))

N = len(points)

attention_offsets = { }

idxs = []

for p1 in points:

for p2 in points:

offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))

if offset not in attention_offsets:

attention_offsets[offset] = len(attention_offsets)

idxs.append(attention_offsets[offset])

self.attention_biases = torch.nn.Parameter(

torch.zeros(num_heads, len(attention_offsets)))

self.register_buffer('attention_bias_idxs',

torch.LongTensor(idxs).view(N, N))

@torch.no_grad()

def train(self, mode=True):

super().train(mode)

if mode and hasattr(self, 'ab'):

del self.ab

else:

self.ab = self.attention_biases[:, self.attention_bias_idxs]

def forward(self, x): # x (B,C,H,W)

B, C, H, W = x.shape

trainingab = self.attention_biases[:, self.attention_bias_idxs]

feats_in = x.chunk(len(self.qkvs), dim=1)

feats_out = []

feat = feats_in[0]

for i, qkv in enumerate(self.qkvs):

if i > 0: # add the previous output to the input

feat = feat + feats_in[i]

feat = qkv(feat)

q, k, v = feat.view(B, -1, H, W).split([self.key_dim, self.key_dim, self.d], dim=1) # B, C/h, H, W

q = self.dws[i](q)

q, k, v = q.flatten(2), k.flatten(2), v.flatten(2) # B, C/h, N

attn = (

(q.transpose(-2, -1) @ k) * self.scale

+

(trainingab[i] if self.training else self.ab[i])

)

attn = attn.softmax(dim=-1) # BNN

feat = (v @ attn.transpose(-2, -1)).view(B, self.d, H, W) # BCHW

feats_out.append(feat)

x = self.proj(torch.cat(feats_out, 1))

return x

class LocalWindowAttention(torch.nn.Module):

r""" Local Window Attention.

Args:

dim (int): Number of input channels.

key_dim (int): The dimension for query and key.

num_heads (int): Number of attention heads.

attn_ratio (int): Multiplier for the query dim for value dimension.

resolution (int): Input resolution.

window_resolution (int): Local window resolution.

kernels (List[int]): The kernel size of the dw conv on query.

"""

def __init__(self, dim, key_dim, num_heads=8,

attn_ratio=4,

resolution=14,

window_resolution=7,

kernels=[5, 5, 5, 5],):

super().__init__()

self.dim = dim

self.num_heads = num_heads

self.resolution = resolution

assert window_resolution > 0, 'window_size must be greater than 0'

self.window_resolution = window_resolution

self.attn = CascadedGroupAttention(dim, key_dim, num_heads,

attn_ratio=attn_ratio,

resolution=window_resolution,

kernels=kernels,)

def forward(self, x):

B, C, H, W = x.shape

if H <= self.window_resolution and W <= self.window_resolution:

x = self.attn(x)

else:

x = x.permute(0, 2, 3, 1)

pad_b = (self.window_resolution - H %

self.window_resolution) % self.window_resolution

pad_r = (self.window_resolution - W %

self.window_resolution) % self.window_resolution

padding = pad_b > 0 or pad_r > 0

if padding:

x = torch.nn.functional.pad(x, (0, 0, 0, pad_r, 0, pad_b))

pH, pW = H + pad_b, W + pad_r

nH = pH // self.window_resolution

nW = pW // self.window_resolution

# window partition, BHWC -> B(nHh)(nWw)C -> BnHnWhwC -> (BnHnW)hwC -> (BnHnW)Chw

x = x.view(B, nH, self.window_resolution, nW, self.window_resolution, C).transpose(2, 3).reshape(

B * nH * nW, self.window_resolution, self.window_resolution, C

).permute(0, 3, 1, 2)

x = self.attn(x)

# window reverse, (BnHnW)Chw -> (BnHnW)hwC -> BnHnWhwC -> B(nHh)(nWw)C -> BHWC

x = x.permute(0, 2, 3, 1).view(B, nH, nW, self.window_resolution, self.window_resolution,

C).transpose(2, 3).reshape(B, pH, pW, C)

if padding:

x = x[:, :H, :W].contiguous()

x = x.permute(0, 3, 1, 2)

return x

class EfficientViTBlock(torch.nn.Module):

""" A basic EfficientViT building block.

Args:

type (str): Type for token mixer. Default: 's' for self-attention.

ed (int): Number of input channels.

kd (int): Dimension for query and key in the token mixer.

nh (int): Number of attention heads.

ar (int): Multiplier for the query dim for value dimension.

resolution (int): Input resolution.

window_resolution (int): Local window resolution.

kernels (List[int]): The kernel size of the dw conv on query.

"""

def __init__(self, type,

ed, kd, nh=8,

ar=4,

resolution=14,

window_resolution=7,

kernels=[5, 5, 5, 5],):

super().__init__()

self.dw0 = Residual(Conv2d_BN(ed, ed, 3, 1, 1, groups=ed, bn_weight_init=0., resolution=resolution))

self.ffn0 = Residual(FFN(ed, int(ed * 2), resolution))

if type == 's':

self.mixer = Residual(LocalWindowAttention(ed, kd, nh, attn_ratio=ar, \

resolution=resolution, window_resolution=window_resolution, kernels=kernels))

self.dw1 = Residual(Conv2d_BN(ed, ed, 3, 1, 1, groups=ed, bn_weight_init=0., resolution=resolution))

self.ffn1 = Residual(FFN(ed, int(ed * 2), resolution))

def forward(self, x):

return self.ffn1(self.dw1(self.mixer(self.ffn0(self.dw0(x)))))

class EfficientViT(torch.nn.Module):

def __init__(self, img_size=400,

patch_size=16,

frozen_stages=0,

in_chans=3,

stages=['s', 's', 's'],

embed_dim=[64, 128, 192],

key_dim=[16, 16, 16],

depth=[1, 2, 3],

num_heads=[4, 4, 4],

window_size=[7, 7, 7],

kernels=[5, 5, 5, 5],

down_ops=[['subsample', 2], ['subsample', 2], ['']],

pretrained=None,

distillation=False,):

super().__init__()

resolution = img_size

self.patch_embed = torch.nn.Sequential(Conv2d_BN(in_chans, embed_dim[0] // 8, 3, 2, 1, resolution=resolution), torch.nn.ReLU(),

Conv2d_BN(embed_dim[0] // 8, embed_dim[0] // 4, 3, 2, 1, resolution=resolution // 2), torch.nn.ReLU(),

Conv2d_BN(embed_dim[0] // 4, embed_dim[0] // 2, 3, 2, 1, resolution=resolution // 4), torch.nn.ReLU(),

Conv2d_BN(embed_dim[0] // 2, embed_dim[0], 3, 1, 1, resolution=resolution // 8))

resolution = img_size // patch_size

attn_ratio = [embed_dim[i] / (key_dim[i] * num_heads[i]) for i in range(len(embed_dim))]

self.blocks1 = []

self.blocks2 = []

self.blocks3 = []

for i, (stg, ed, kd, dpth, nh, ar, wd, do) in enumerate(

zip(stages, embed_dim, key_dim, depth, num_heads, attn_ratio, window_size, down_ops)):

for d in range(dpth):

eval('self.blocks' + str(i+1)).append(EfficientViTBlock(stg, ed, kd, nh, ar, resolution, wd, kernels))

if do[0] == 'subsample':

#('Subsample' stride)

blk = eval('self.blocks' + str(i+2))

resolution_ = (resolution - 1) // do[1] + 1

blk.append(torch.nn.Sequential(Residual(Conv2d_BN(embed_dim[i], embed_dim[i], 3, 1, 1, groups=embed_dim[i], resolution=resolution)),

Residual(FFN(embed_dim[i], int(embed_dim[i] * 2), resolution)),))

blk.append(PatchMerging(*embed_dim[i:i + 2], resolution))

resolution = resolution_

blk.append(torch.nn.Sequential(Residual(Conv2d_BN(embed_dim[i + 1], embed_dim[i + 1], 3, 1, 1, groups=embed_dim[i + 1], resolution=resolution)),

Residual(FFN(embed_dim[i + 1], int(embed_dim[i + 1] * 2), resolution)),))

self.blocks1 = torch.nn.Sequential(*self.blocks1)

self.blocks2 = torch.nn.Sequential(*self.blocks2)

self.blocks3 = torch.nn.Sequential(*self.blocks3)

self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]

def forward(self, x):

outs = []

x = self.patch_embed(x)

x = self.blocks1(x)

outs.append(x)

x = self.blocks2(x)

outs.append(x)

x = self.blocks3(x)

outs.append(x)

return outs

EfficientViT_m0 = {

'img_size': 224,

'patch_size': 16,

'embed_dim': [64, 128, 192],

'depth': [1, 2, 3],

'num_heads': [4, 4, 4],

'window_size': [7, 7, 7],

'kernels': [7, 5, 3, 3],

}

EfficientViT_m1 = {

'img_size': 224,

'patch_size': 16,

'embed_dim': [128, 144, 192],

'depth': [1, 2, 3],

'num_heads': [2, 3, 3],

'window_size': [7, 7, 7],

'kernels': [7, 5, 3, 3],

}

EfficientViT_m2 = {

'img_size': 224,

'patch_size': 16,

'embed_dim': [128, 192, 224],

'depth': [1, 2, 3],

'num_heads': [4, 3, 2],

'window_size': [7, 7, 7],

'kernels': [7, 5, 3, 3],

}

EfficientViT_m3 = {

'img_size': 224,

'patch_size': 16,

'embed_dim': [128, 240, 320],

'depth': [1, 2, 3],

'num_heads': [4, 3, 4],

'window_size': [7, 7, 7],

'kernels': [5, 5, 5, 5],

}

EfficientViT_m4 = {

'img_size': 224,

'patch_size': 16,

'embed_dim': [128, 256, 384],

'depth': [1, 2, 3],

'num_heads': [4, 4, 4],

'window_size': [7, 7, 7],

'kernels': [7, 5, 3, 3],

}

EfficientViT_m5 = {

'img_size': 224,

'patch_size': 16,

'embed_dim': [192, 288, 384],

'depth': [1, 3, 4],

'num_heads': [3, 3, 4],

'window_size': [7, 7, 7],

'kernels': [7, 5, 3, 3],

}

def EfficientViT_M0(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m0):code>

model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)

if pretrained:

model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))

if fuse:

replace_batchnorm(model)

return model

def EfficientViT_M1(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m1):code>

model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)

if pretrained:

model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))

if fuse:

replace_batchnorm(model)

return model

def EfficientViT_M2(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m2):code>

model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)

if pretrained:

model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))

if fuse:

replace_batchnorm(model)

return model

def EfficientViT_M3(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m3):code>

model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)

if pretrained:

model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))

if fuse:

replace_batchnorm(model)

return model

def EfficientViT_M4(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m4):code>

model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)

if pretrained:

model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))

if fuse:

replace_batchnorm(model)

return model

def EfficientViT_M5(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m5):code>

model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)

if pretrained:

model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))

if fuse:

replace_batchnorm(model)

return model

def update_weight(model_dict, weight_dict):

idx, temp_dict = 0, { }

for k, v in weight_dict.items():

# k = k[9:]

if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):

temp_dict[k] = v

idx += 1

model_dict.update(temp_dict)

print(f'loading weights... { idx}/{ len(model_dict)} items')

return model_dict

在ultralytics/nn/tasks.py中导入刚才的efficientVit模块：

# 主干网络

from ultralytics.nn.backbone.efficientViT import *

第2步–修改tasks.py中的相关内容

<code> if isinstance(c2, list):

is_backbone = True

m_ = m

m_.backbone = True

else:

m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module

t = str(m)[8:-2].replace('__main__.', '') # module type

m.np = sum(x.numel() for x in m_.parameters()) # number params

m_.i, m_.f, m_.type = i + 4 if is_backbone else i, f, t # attach index, 'from' index, type

if verbose:

LOGGER.info(f'{ i:>3}{ str(f):>20}{ n_:>3}{ m.np:10.0f} { t:<45}{ str(args):<30}') # print

save.extend(x % (i + 4 if is_backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist

layers.append(m_)

4.修改下面截图部分代码

修改前：

修改后：

修改代码为：

<code> if isinstance(c2, list):

ch.extend(c2)

for _ in range(5 - len(ch)):

ch.insert(0, 0)

else:

ch.append(c2)

_predict_once函数修改

替换ultralytics/nn/tasks.py中的BaseModel类的_predict_once函数，代码如下：

def _predict_once(self, x, profile=False, visualize=False):

"""

Perform a forward pass through the network.

Args:

x (torch.Tensor): The input tensor to the model.

profile (bool): Print the computation time of each layer if True, defaults to False.

visualize (bool): Save the feature maps of the model if True, defaults to False.

Returns:

(torch.Tensor): The last output of the model.

"""

y, dt = [], [] # outputs

for m in self.model:

if m.f != -1: # if not from previous layer

x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers

if profile:

self._profile_one_layer(m, x, dt)

if hasattr(m, 'backbone'):

x = m(x)

for _ in range(5 - len(x)):

x.insert(0, None)

for i_idx, i in enumerate(x):

if i_idx in self.save:

y.append(i)

else:

y.append(None)

# for i in x:

# if i is not None:

# print(i.size())

x = x[-1]

else:

x = m(x) # run

y.append(x if m.i in self.save else None) # save output

if visualize:

feature_visualization(x, m.type, m.i, save_dir=visualize)

return x

第3步：创建配置文件–yolov8-efficientViT.yaml

在ultralytics/cfg/models/v8目录下，创建新的配置文件yolov8-efficientViT.yaml，内容如下：

注：可以使用EfficientViT_M0, EfficientViT_M1, EfficientViT_M2, EfficientViT_M3, EfficientViT_M4, EfficientViT_M5中的任何一个，参数量不同。

<code># 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

# 0-P1/2

# 1-P2/4

# 2-P3/8

# 3-P4/16

# 4-P5/32

# YOLOv8.0n backbone

backbone:

# [from, repeats, module, args]

- [-1, 1, EfficientViT_M0, []] # 4

- [-1, 1, SPPF, [1024, 5]] # 5

# YOLOv8.0n head

head:

- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 6

- [[-1, 3], 1, Concat, [1]] # 7 cat backbone P4

- [-1, 3, C2f, [512]] # 8

- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 9

- [[-1, 2], 1, Concat, [1]] # 10 cat backbone P3

- [-1, 3, C2f, [256]] # 11 (P3/8-small)

- [-1, 1, Conv, [256, 3, 2]] # 12

- [[-1, 8], 1, Concat, [1]] # 13 cat head P4

- [-1, 3, C2f, [512]] # 14 (P4/16-medium)

- [-1, 1, Conv, [512, 3, 2]] # 15

- [[-1, 5], 1, Concat, [1]] # 16 cat head P5

- [-1, 3, C2f, [1024]] # 17 (P5/32-large)

- [[11, 14, 17], 1, Detect, [nc]] # Detect(P3, P4, P5)

yolov8.yaml与yolov8-efficientViT.yaml对比

backbone部分：yolov8.yaml与yolov8-efficientViT.yaml对比：

head部分：<code>yolov8.yaml与yolov8-efficientViT.yaml对比：【注意层数的变化，所以要修改对应的层数数字部分】

第4步：加载配置文件训练模型

运行训练代码<code>train.py文件，内容如下：

#coding:utf-8

# 替换主干网络，训练

from ultralytics import YOLO

if __name__ == '__main__':

model = YOLO('ultralytics/cfg/models/v8/yolov8-efficientViT.yaml')

model.load('yolov8n.pt') # loading pretrain weights

model.train(data='datasets/TomatoData/data.yaml', epochs=250, batch=4)code>

第5步：模型推理

<code>#coding:utf-8

from ultralytics import YOLO

import cv2

# 所需加载的模型目录

# path = 'models/best2.pt'

path = 'runs/detect/train9/weights/best.pt'

# 需要检测的图片地址

img_path = "TestFiles/Riped tomato_31.jpeg"

# 加载预训练模型

# conf0.25object confidence threshold for detection

# iou0.7intersection over union (IoU) threshold for NMS

model = YOLO(path, task='detect')code>

# 检测图片

results = model(img_path)

res = results[0].plot()

# res = cv2.resize(res,dsize=None,fx=2,fy=2,interpolation=cv2.INTER_LINEAR)

cv2.imshow("YOLOv8 Detection", res)

cv2.waitKey(0)

【源码获取】