The autoanchor result is not same as anchors in model?

[PonyPC]

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Question

v6.1
The autoanchor result is not same as anchors in model.
I trained my data with autoanchor, and get this:

�[34m�[1mAutoAnchor: �[0mn=9, img_size=640, metric_all=0.571/0.910-mean/best, past_thr=0.583-mean: 25,97, 95,33, 35,95, 45,90, 86,52, 58,82, 73,68, 52,135, 106,81

After training, I print anchor from model but get:

YOLOv5  2022-2-22 torch 1.8.1+cu111 CPU

tensor([[[ 18.29688,  17.03125],
         [ 12.88281,  33.62500],
         [ 26.46875,  20.29688]],

        [[ 44.90625,  90.37500],
         [ 85.81250,  51.71875],
         [ 58.37500,  81.87500]],

        [[100.62500, 388.50000],
         [381.25000, 132.25000],
         [139.75000, 379.50000]]])

Only P4 same.

Additional

I got the log from models\hub\anchors.yaml but P3/P4/P5/P6 are same:

# P6-640:  thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11,  21,19,  17,41,  43,32,  39,70,  86,64,  65,131,  134,130,  120,265,  282,180,  247,354,  512,387
anchors_p6_640:
  - [9,11,  21,19,  17,41]  # P3/8
  - [43,32,  39,70,  86,64]  # P4/16
  - [65,131,  134,130,  120,265]  # P5/32
  - [282,180,  247,354,  512,387]  # P6/64

I also print the offical release model yolov5n.pt, both anchors in model and trainning autoanchor prints are same.

Is it normal?

[PonyPC]

It seems only P4/16 are same.

[zhiqwang]

Hi @PonyPC

Could you try the following method to compute the anchor_grids:

num_anchors = checkpoint_yolov5.model[-1].anchors.shape[1]
anchor_grids = (
    (checkpoint_yolov5.model[-1].anchors * checkpoint_yolov5.model[-1].stride.view(-1, 1, 1))
    .reshape(1, -1, 2 * num_anchors)
    .tolist()[0]
)

[PonyPC]

@zhiqwang Hi, I got same result as above

[zhiqwang]

You show more than one result above, I can't determine which one it is, is it convenient to post a more detailed result?

[PonyPC]

[[18.296875, 17.03125, 12.8828125, 33.625, 26.46875, 20.296875], [44.90625, 90.375, 85.8125, 51.71875, 58.375, 81.875], [100.625, 388.5, 381.25, 132.25, 139.75, 379.5]]

@zhiqwang This is what your code output

[zhiqwang]

Hi @PonyPC

Got it, the result in "anchors.yaml" should be calculated from the coco dataset, I guess the result here is normal. The actual computation will automatically determine the actual anchors computed by the AutoAnchor mechanism:

- [18.296875,17.03125, 12.8828125,33.625, 26.46875,20.296875]  # P3/8
- [44.90625,90.375, 85.8125,51.71875, 58.375,81.875]  # P4/16
- [100.625,388.5, 381.25,132.25, 139.75,379.5  # P5/32

[PonyPC]

@zhiqwang
But autoanchor.py printed is

�[34m�[1mAutoAnchor: �[0mthr=0.25: 1.0000 best possible recall, 8.71 anchors past thr
�[34m�[1mAutoAnchor: �[0mn=9, img_size=640, metric_all=0.571/0.910-mean/best, past_thr=0.583-mean: 25,97, 95,33, 35,95, 45,90, 86,52, 58,82, 73,68, 52,135, 106,81
�[34m�[1mAutoAnchor: �[0mReversing anchor order
�[34m�[1mAutoAnchor: �[0mNew anchors saved to model. Update model *.yaml to use these anchors in the future.

[PonyPC]

I assume it should be past_thr=0.583-mean: 18,17, 13,34, 26,22, 45,90, 86,52, 58,82, 101, 389, 381,132, 140,380
Only P4 same. I tried offical coco model yolov5n.pt, they are same.
As you can see models\hub\anchors.yml is also same.
So I doubt something is wrong with it?

[PonyPC]

@glenn-jocher

[glenn-jocher]

@PonyPC YOLOv5 🚀 anchors are saved as Detect() layer attributes on model creation, and updated as necessary by AutoAnchor before training starts. Their exact location is here:

yolov5/models/yolo.py

Line 45 in f17c86b

self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)

You can examine the anchors of any trained YOLOv5 model like this:

Input

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)  # official model
model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt',  autoshape=False)  # custom model

# Anchors
m = model.model.model[-1]  # Detect() layer
print(m.anchors * m.stride.view(-1, 1, 1))  # print anchors

Output

YOLOv5 🚀 2021-11-22 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
#           x     y
tensor([[[ 10.,  13.],
         [ 16.,  30.],
         [ 33.,  23.]],  # P3/8-small

        [[ 30.,  61.],
         [ 62.,  45.],
         [ 59., 119.]],  # P4/16-medium

        [[116.,  90.],
         [156., 198.],
         [373., 326.]]], dtype=torch.float16)  # P5/32-large

Example

Good luck 🍀 and let us know if you have any other questions!

[PonyPC]

I know what you mean. But there are different in my case which you circled it at last image.
@glenn-jocher

[glenn-jocher]

@PonyPC 👋 hi, thanks for letting us know about this possible problem with YOLOv5 🚀. We've created a few short guidelines below to help users provide what we need in order to get started investigating a possible problem.

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