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I'm training a model for license plate character detection, this is the training command I use,

python train.py
--img 160
--batch 256
--epochs 300
--data data/number_plate_00_02_08_09_18_19_all_mixing.yaml
--weights ''
--cfg models/yolov5n-number_plate_00_02_08_09_18_19_all_mixing_160.yaml
--hyp data/hyps/hyp.scratch-low-number_plate_00_02_08_09_18_19_all_mixing.yaml

When the training is completed, I found that its anchors use the default anchors, and there is no automatic recalculation.

import torch
from models.experimental import attempt_load

model = attempt_load('./yolov5n-number_plate_00_02_08_09_18_19_all_mixing_best_0317_160x160.pt', map_location=torch.device('cpu'))
m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]
print(m.anchor_grid)

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

I don't think it's reasonable, this model is very bad at detecting small objects,

Since there are no autoanchors during training, I recalculate the anchors on my dataset,

import utils.autoanchor as autoAC

new_anchors = autoAC.kmean_anchors('./data/number_plate_00_02_08_09_18_19_all_mixing.yaml', 9, 160, 5.0, 5000, True)
print(new_anchors)

Get the following new anchors,

• [17,21, 19,35, 18,40] # P3/8
• [22,36, 21,40, 25,38] # P4/16
• [24,42, 28,40, 29,50] # P5/32

I go to yolov5/models/yolov5n.yaml,

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

replace with

• [17,21, 19,35, 18,40] # P3/8
• [22,36, 21,40, 25,38] # P4/16
• [24,42, 28,40, 29,50] # P5/32

and retrain,

python train.py
--img 160
--batch 256
--epochs 300
--data data/number_plate_00_02_08_09_18_19_all_mixing.yaml
--weights ''
--cfg models/yolov5n-number_plate_00_02_08_09_18_19_all_mixing_160.yaml
--hyp data/hyps/hyp.scratch-low-number_plate_00_02_08_09_18_19_all_mixing.yaml
--noautoanchor

After the training, I re-checked best.pt/last.pt and got:

• [6,11, 7,10, 7,13] # P3/8
• [22,36, 21,40, 25,38] # P4/16
• [68,84, 76,140, ​​72,160] # P5/32

I used "--noautoanchor" during training, but the model was not constructed based on the anchors I specified? The model also recomputed a new set of anchors?

I refer to #6966