This model uses a small-footprint network trained end-to-end to recognize Chinese license plates in traffic.
1165 Chinese plates from different provinces
Note: The license plates on the image were modified to protect the owners' privacy.
Metric | Value |
---|---|
Rotation in-plane | ±10˚ |
Rotation out-of-plane | Yaw: ±45˚ / Pitch: ±45˚ |
Min plate width | 94 pixels |
Ratio of correct reads | 88.58% |
GFlops | 0.328 |
MParams | 1.218 |
Source framework | Caffe* |
Only "blue" license plates, which are common in public, were tested thoroughly. Other types of license plates may underperform.
-
Image, name:
data
, shape:1, 3, 24, 94
in the format1, C, H, W
, where:C
- number of channelsH
- image heightW
- image width
Expected color order is
BGR
. -
An auxiliary blob that is needed for correct decoding, name:
seq_ind
, shape:88,1
. Set this to[1, 1, 1, ..., 1]
.
Encoded vector of floats, name: dec
, shape: 1, 88, 1, 1
. Each float
is an integer number encoding a character according to this dictionary:
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 <Anhui>
11 <Beijing>
12 <Chongqing>
13 <Fujian>
14 <Gansu>
15 <Guangdong>
16 <Guangxi>
17 <Guizhou>
18 <Hainan>
19 <Hebei>
20 <Heilongjiang>
21 <Henan>
22 <HongKong>
23 <Hubei>
24 <Hunan>
25 <InnerMongolia>
26 <Jiangsu>
27 <Jiangxi>
28 <Jilin>
29 <Liaoning>
30 <Macau>
31 <Ningxia>
32 <Qinghai>
33 <Shaanxi>
34 <Shandong>
35 <Shanghai>
36 <Shanxi>
37 <Sichuan>
38 <Tianjin>
39 <Tibet>
40 <Xinjiang>
41 <Yunnan>
42 <Zhejiang>
43 <police>
44 A
45 B
46 C
47 D
48 E
49 F
50 G
51 H
52 I
53 J
54 K
55 L
56 M
57 N
58 O
59 P
60 Q
61 R
62 S
63 T
64 U
65 V
66 W
67 X
68 Y
69 Z
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
[*] Other names and brands may be claimed as the property of others.