This model uses a small-footprint network trained end-to-end to recognize Chinese license plates in traffic.
300320 Sythetic Chinese plates i.e. the plate text on them consists of symbols generated randomly (but to conform to the plate requirements in terms of the number of characters, sequence, shape, placement, etc.). The "real-looking" appearance of the plates (rotation, dirt, color, lighting, etc.) is achieved by a style transfer procedure.
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 | 98% |
GFlops | 0.347 |
MParams | 1.435 |
Source framework | TensorFlow* |
Only "blue" license plates, which are common in public, were tested thoroughly. Other types of license plates may underperform.
Image, name: input
, shape: 1, 24, 94, 3
, format is 1, H, W, C
, where:
C
- channelH
- heightW
- width
Channel order is BGR
.
Image, name: input
, shape: 1, 24, 94, 3
, format is 1, H, W, C
, where:
C
- channelH
- heightW
- width
Channel order is BGR
.
Encoded vector of floats, name: d_predictions
, 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
Encoded vector of integers, name: d_predictions:0
, shape: 1, 88
. Each value
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
You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
omz_downloader --name <model_name>
An example of using the Model Converter:
omz_converter --name <model_name>
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
The original model is distributed under the
Apache License, Version 2.0.
A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0.txt
.
[*] Other names and brands may be claimed as the property of others.