@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