单阶段目标检测|YOLOv5-v6.0-网络架构详解（第二篇） YOLO|YOLOv5|YOLOv5|深度学习|#

参考：YOLOv5-5.0v-yaml 解析及模型构建(第二篇)_星魂非梦的博客-CSDN博客
前文：YOLOv5-v6.0-yolov5s网络架构详解（第一篇）_星魂非梦的博客-CSDN博客_yolov5s网络结构
本文目的是画出更规范的架构图。前文的不太规范。
1. v6.0相比v5.0的重要更新：Releases · ultralytics/yolov5 · GitHub

Roboflow Integration ? NEW: Train YOLOv5 models directly on any Roboflow dataset with our new integration(集成)! (#4975 by @Jacobsolawetz)
YOLOv5n 'Nano' models ? NEW: New smaller YOLOv5n (1.9M params) model below YOLOv5s (7.5M params), exports to 2.1 MB INT8 size, ideal for ultralight(超轻量级) mobile(移动端) solutions. (#5027 by @glenn-jocher)
TensorFlow and Keras Export: TensorFlow, Keras, TFLite, TF.js model export now fully integrated(集成的) using python export.py --include saved_model pb tflite tfjs (#1127 by @zldrobit)
OpenCV DNN: YOLOv5 ONNX models are now compatible(兼容) with both OpenCV DNN and ONNX Runtime (#4833 by @SamFC10).
Model Architecture: Updated backbones are slightly smaller, faster and more accurate.
- Replacement of Focus() with an equivalent(等同的) Conv(k=6, s=2, p=2) layer (#4825 by @thomasbi1) for improved exportability(可移植性)
- New SPPF() replacement for SPP() layer for reduced ops (#4420 by @glenn-jocher)
- Reduction in P3 backbone layer C3() repeats from 9 to 6 for improved speeds
- Reorder(重新排序) places SPPF() at end of backbone
- Reintroduction of shortcut in the last C3() backbone layer
- Updated hyperparameters with increased mixup and copy-paste augmentation

本文只关注Model Architecture的改变。
2. 配置文件：models/yolov5s.yaml

# YOLOv5by Ultralytics, GPL-3.0 license# Parameters nc: 80# number of classes depth_multiple: 0.33# model depth multiple width_multiple: 0.50# layer channel multiple anchors: - [10,13, 16,30, 33,23]# P3/8 - [30,61, 62,45, 59,119]# P4/16 - [116,90, 156,198, 373,326]# P5/32# YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [64, 6, 2, 2]],# 0-P1/2 [-1, 1, Conv, [128, 3, 2]],# 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]],# 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]],# 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]],# 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]],# 9 ]# YOLOv5 v6.0 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]],# cat backbone P4 [-1, 3, C3, [512, False]],# 13[-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]],# cat backbone P3 [-1, 3, C3, [256, False]],# 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5) ]

【单阶段目标检测|YOLOv5-v6.0-网络架构详解（第二篇）】2.1 Replacement of Focus() with an equivalent(等同的) Conv(k=6, s=2, p=2) layer (#4825 by @thomasbi1) for improved exportability(可移植性)

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2.2 New SPPF() replacement for SPP() layer for reduced ops (#4420 by @glenn-jocher)

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2.3 Reduction in P3 backbone layer C3() repeats from 9 to 6 for improved speeds

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3. 总架构图

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yolov5-5.0 架构图

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yolov5-6.0 架构图

Reorder(重新排序) places SPPF() at end of backbone
Reintroduction of shortcut in the last C3() backbone layer

从两个图可知：6.0 将SPPF()放在backbone的最后；8模块为C3_1 引进了 shortcut。
补充：数据增强部分：increased mixup and copy-paste augmentation
4. 推理以上架构图为模型训练时候的图，在模型推理时候，models/yolo.py--Detect类中，会把3个head的输出进行 cat。
解释参考：YOLOv5-5.0v-yaml 解析及模型构建(第二篇)_星魂非梦的博客-CSDN博客

if not self.training:# inference if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic: self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)y = x[i].sigmoid() if self.inplace: y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]# xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]# wh else:# for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]# xy wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]# wh y = torch.cat((xy, wh, y[..., 4:]), -1) z.append(y.view(bs, -1, self.no))