The ssdlite_mobilenet_v2
model is used for object detection. For details, see the paper, MobileNetV2: Inverted Residuals and Linear Bottlenecks.
Metric | Value |
---|---|
Type | Detection |
GFLOPs | 1.525 |
MParams | 4.475 |
Source framework | TensorFlow* |
Metric | Value |
---|---|
coco_precision | 24.2946% |
Image, name: image_tensor
, shape: 1, 300, 300, 3
, format: B, H, W, C
, where:
B
- batch sizeH
- image heightW
- image widthC
- number of channels
Expected color order: RGB
.
Image, name: image_tensor
, shape: 1, 300, 300, 3
, format: B, H, W, C
, where:
B
- batch sizeH
- image heightW
- image widthC
- number of channels
Expected color order: BGR
.
- Classifier, name:
detection_classes
. Contains predicted bounding-boxes classes in a range [1, 91]. The model was trained on Common Objects in Context (COCO) dataset version with 91 categories of object, 0 class is for background. Mapping to class names provided in<omz_dir>/data/dataset_classes/coco_91cl_bkgr.txt
file. - Probability, name:
detection_scores
. Contains probability of detected bounding boxes. - Detection box, name:
detection_boxes
. Contains detection boxes coordinates in format[y_min, x_min, y_max, x_max]
, where (x_min
,y_min
) are coordinates of the top left corner, (x_max
,y_max
) are coordinates of the right bottom corner. Coordinates are rescaled to input image size. - Detections number, name:
num_detections
. Contains the number of predicted detection boxes.
The array of summary detection information, name: DetectionOutput
, shape: 1, 1, 100, 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 ID in range [1, 91], mapping to class names provided in<omz_dir>/data/dataset_classes/coco_91cl_bkgr.txt
fileconf
- confidence for the predicted class- (
x_min
,y_min
) - coordinates of the top left bounding box corner (coordinates are stored in a normalized format, in a range [0, 1]) - (
x_max
,y_max
) - coordinates of the bottom right bounding box corner (coordinates are stored in a normalized format, in a range [0, 1])
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:
- Object Detection C++ Demo
- Object Detection Python* Demo
- Pedestrian Tracker C++ Demo
- Single Human Pose Estimation Demo
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-TF-Models.txt
.