"S of 43.2. Compared will
posed method demonstrates a
md a 1.5 times improvement in
schieves the highest mAP among
ming precise detection in various
kage methods (category 3), which
R-CNN, Faster R-CNN, and Cas-
osed model achieves the highest mAP
2.8% over that of Improved Mask
31:44 UTC from IEEE Xplore. Restrictions apply.
Rs, SSA-YOLOMAP30.988,
在MAP方面分别超过Faster R-
CNN CRNMSOCascade R-
CNN-CR-NMS 3.5%03.6%, 33
验结果表明,提出的模型优于两
阶段法。
fect categories such as Cr, In
Pa, Ps, and Rs. 5SA-YOLO
achieves a mAP of 0.988
15.2.0.63064_...
= _init_cpython-312.pyc
216
217
CAL
解析
E block.cpython-312.pyc
219
E comcpython-312.pyt
PPT
220
AnomalyBERT-
main.zip
Ehead.cpython-312.pyc
221
transformer.cpython-312.pyc
222
activation.cpython-312.pyc
218 class MSDeformAttn (nn.Module):
Multiscale Deformable Attention Module based on Deformable-DETR and PaddleDetection imple
https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms deform
Eutils.cpython-312.pyc
223
输出
调试控制台 终端
端口
activation.py
all
174
429
AnomalyBERT-
block.py
surpassing Faster R
CNN-CRNMS and Cascade R-
CNN-CR-NMS by 3.5% and
3.6% in mAP, respectively.
main
conv.py
Epoch
GPU mem
box loss
head.py
295/300
4.11G
cls loss
0.9496 0.7386
transformer.py
Class
all
Images Instances
174
5040017
These results demonstrate
DETECTION
that our proposed model
outperforms two-stage
methods in steel surface
defect detection,
utils.py
429
0.707
dfl loss Instances
1.35
Box (P
0.693
0.687
0.744
Size
57
640: 100%
MAP50 mAP50-95): 100%
50/50 [00:05<
3/3
0.699
0.741
0.407
Epoch
autobackend.py
296/300
BIFPN.py
P4
Schematic of improved module placement in the backbone network.
his part, we evaluate the impact of the CSE module on
-rolled strip surface defect detection.
In the original YOLOVSS backbone architecture, there are
tasks.py
GPU mem
14G
Class
all
box loss
0.9402
cls_loss
0.7169
dfl loss Instances
Size
Images Instances
174
429
1.342
Box (P
0.683
66
640: 100%
R
0.7
MAP50 MAP50-95): 100%
0.744
50/50 [00:04<00:0
3/3 [00
0.413
Epoch
297/300
GPU_mem
4.110
Class
all
box loss
0.9564
Images Instances
174
cls loss
0.7376
dfl loss Instances
Size
429
1.363
Box (P
0.705
63
R
640: 100%
0.694
0.749
MAP50 MAP50-95): 100%
50/50 [00:04<00:00,
0.411
3/3 [00:06
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