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@sirius-ai Thank you very much for implementing LPRNet. During the training process, adding residual connection to SmallBasicBlock using ResNet can further improve performance (LPRNet ->LPRNetPlus); Meanwhile, adding the STNet module can further enhance the evaluation results of CCPD (LPRNet+STNet).
Model
ARCH
Input Shape
GFLOPs
Model Size (MB)
ChineseLicensePlate Accuracy (%)
Training Data
Testing Data
CRNN
CONV+GRU
(3, 48, 168)
4.0
58
82.147
269,621
149,002
CRNN_Tiny
CONV+GRU
(3, 48, 168)
0.3
4.0
76.590
269,621
149,002
LPRNetPlus
CONV
(3, 24, 94)
0.5
2.3
63.546
269,621
149,002
LPRNet
CONV
(3, 24, 94)
0.3
1.9
60.105
269,621
149,002
LPRNetPlus+STNet
CONV
(3, 24, 94)
0.5
2.5
72.130
269,621
149,002
LPRNet+STNet
CONV
(3, 24, 94)
0.3
2.2
72.261
269,621
149,002
The relevant code has been open sourced and can be viewed at zjykzj/crnn-ctc
The text was updated successfully, but these errors were encountered:
@zjykzj Amazing guys, I just at the beginning of the task, and I think it have not been update for 6 years, there must be some common method to improve it, and I see your detailed exp result. Great Work!
@sirius-ai Thank you very much for implementing LPRNet. During the training process, adding residual connection to SmallBasicBlock using ResNet can further improve performance (LPRNet ->LPRNetPlus); Meanwhile, adding the STNet module can further enhance the evaluation results of CCPD (LPRNet+STNet).
The relevant code has been open sourced and can be viewed at zjykzj/crnn-ctc
The text was updated successfully, but these errors were encountered: