【目标检测-YOLO】YOLOv5-6.0-P6 1280 Models(第三篇)
5.0 版本发布了 YOLOv5-P6 模型。
This release implements YOLOv5-P6 models and retrained YOLOv5-P5 models. All model sizes YOLOv5s/m/l/x are now available in both P5 and P6 architectures:
- YOLOv5-P6 models: 4 output layers P3, P4, P5, P6 at strides 8, 16, 32, 64 trained at
--img 1280
python detect.py --weights yolov5s6.pt # P6 models
yolov5m6.pt
yolov5l6.pt
yolov5x6.pt
作者：YOLOv5 P6 Models · Issue #2110 · ultralytics/yolov5 · GitHub
We've done a bit of experimentation with adding an additional large object output layer P6, following the EfficientDet example of increasing output layers for larger models, except in our case applying it to all models. The current models have P3 (stride 8, small) to P5 (stride 32, large) outputs. The P6 output layer is stride 64 and intended for extra-large objects. It seems to help normal COCO training at --img 640, and we've also gotten good results training at --img 1280.
The architecture changes we've made to add a P6 layer are here. The backbone is extended down to P6, and the PANet head goes to down to P3 (like usual) and back up to P6 now instead of stopping at P5. New anchors were also added, evolved at --img 1280.
由于已经发布6.0版本，因此本文以6.0版本的 yolov5s6 作为标准。
1. 配置文件
models/hub/yolov5s6.yaml
# YOLOv5 by 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:
- [19,27, 44,40, 38,94] # P3/8
- [96,68, 86,152, 180,137] # P4/16
- [140,301, 303,264, 238,542] # P5/32
- [436,615, 739,380, 925,792] # P6/64
# 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, [768, 3, 2]], # 7-P5/32
[-1, 3, C3, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 11
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [768, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P5
[-1, 3, C3, [768, False]], # 15
[-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]], # 19
[-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]], # 23 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 20], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 16], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
[-1, 1, Conv, [768, 3, 2]],
[[-1, 12], 1, Concat, [1]], # cat head P6
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
2. 步骤
2.1 下载权重：https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s6.pt
2.2 导出onnx:
python export.py --weights yolov5s6.pt --include onnx --simplify2.3 画架构图
2.4 作者结论
Conclusion is that the P6 models increase performance on COCO under all scenarios we tested, though they are also a bit slower (maybe 10% slower), larger (50% larger), with only slightly more FLOPS though, so training time and CUDA memory requirements are almost the same as the P5 models. We are doing some more studies to see if these might be suitable for replacement of the current models. Let me know if you have any thoughts. For the time being, these models are available for auto-download just like the regular models, i.e.
python detect.py --weights yolov5s6.pt.
注意：Anchors 为 4个， 每个head 分配3个Anchors
anchors:
- [19,27, 44,40, 38,94] # P3/8
- [96,68, 86,152, 180,137] # P4/16
- [140,301, 303,264, 238,542] # P5/32
- [436,615, 739,380, 925,792] # P6/64