This is a MobileNetV2 + SSD-based vehicle and (Chinese) license plate detector for the "Barrier" use case.
Metric | Value |
---|---|
Mean Average Precision (mAP) | 99.52% |
AP vehicles | 99.90% |
AP plates | 99.13% |
Car pose | Front facing cars |
Min plate width | 96 pixels |
Max objects to detect | 200 |
GFlops | 0.271 |
MParams | 0.547 |
Source framework | TensorFlow* |
Average Precision (AP) is defined as an area under the precision/recall curve. Validation dataset is BIT-Vehicle.
An input image, name: input
, shape: 1, 256, 256, 3
, format: B, H, W, C
, where:
B
- batch sizeH
- image heightW
- image widthC
- number of channels
Expected color order: RGB
.
Mean values: [127.5, 127.5, 127.5], scale factor for each channel: 127.5
An input image, name: input
, shape: 1, 3, 256, 256
, format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order is BGR
.
The net outputs a blob with the shape: 1, 1, 200, 7
in the format 1, 1, N, 7
, where N
is the number of detected
bounding boxes. For each detection, the description has the format:
[image_id
, label
, conf
, x_min
, y_min
, x_max
, y_max
], where:
image_id
- ID of the image in the batchlabel
- predicted class IDconf
- confidence for the predicted class- (
x_min
,y_min
) - coordinates of the top left bounding box corner - (
x_max
,y_max
) - coordinates of the bottom right bounding box corner.
The net outputs a blob with the shape: 1, 1, 200, 7
in the format 1, 1, N, 7
, where N
is the number of detected
bounding boxes. For each detection, the description has the format:
[image_id
, label
, conf
, x_min
, y_min
, x_max
, y_max
], where:
image_id
- ID of the image in the batchlabel
- predicted class IDconf
- confidence for the predicted class- (
x_min
,y_min
) - coordinates of the top left bounding box corner - (
x_max
,y_max
) - coordinates of the bottom right bounding box corner.
You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>
An example of using the Model Converter:
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>
The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0.txt.
[*] Other names and brands may be claimed as the property of others.