【魔改YOLOv5-6.x（下）】YOLOv5s+Ghostconv+BiFPN+CA

文章目录

前言

【魔改YOLOv5-6.x（上）】：结合轻量化网络Shufflenetv2、Mobilenetv3和Ghostnet

【魔改YOLOv5-6.x（中）】：加入ACON激活函数、CBAM和CA注意力机制、加权双向特征金字塔BiFPN

本文使用的YOLOv5版本为v6.1，对YOLOv5-6.x网络结构还不熟悉的同学，可以移步至：【YOLOv5-6.x】网络模型&源码解析
 

训练设置

$ python -m torch.distributed.launch --nproc_per_node 2 train.py --weights  --cfg yolov5s.yaml --data data/VOC2007.yaml -- hyp data/hyps/hyp.scratch-high.yaml --epochs 300 --device 0,1

测试设置

$ python val.py --weights yolov5s.pt --data VOC2007.yaml --img 832 --augment --half --iou 0.6
$ python val.py --weights yolov5s.pt --data VOC2007.yaml --img 640 --task speed --batch 1

模型文件（参考）

yolov5s-Ghostconv-BiFPN-CA.yaml

# Parameters
nc: 20  # 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, GhostConv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, GhostConv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, GhostConv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, GhostConv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, CABlock, [1024, 32]],  # 9 CA <-- Coordinate Attention [out_channel, reduction]
   [-1, 1, SPPF, [1024, 5]],  # 10
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, GhostConv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 14

   [-1, 1, GhostConv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 18 (P3/8-small)

   [-1, 1, GhostConv, [256, 3, 2]],
   [[-1, 15, 6], 1, Concat, [1]],  # cat head P4 <--- BiFPN change
   [-1, 3, C3, [512, False]],  # 21 (P4/16-medium)

   [-1, 1, GhostConv, [512, 3, 2]],
   [[-1, 11], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 24 (P5/32-large)

   [[18, 21, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

 
训练仍在进行中，之后会更新相应的测试结果，目前还没有尝试更多的改进方法，欢迎大家前来交流，分享改进YOLOv5的方法~

来源：嗜睡的篠龙