The ssd_mobilenet_v1_coco
model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. The difference between this model and the mobilenet-ssd
is that there the mobilenet-ssd
can only detect face, the ssd_mobilenet_v1_coco
model can detect objects.
Metric | Value |
---|---|
Type | Detection |
GFLOPs | 2.494 |
MParams | 6.807 |
Source framework | TensorFlow* |
Metric | Value |
---|---|
coco_precision | 23.3212% |
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 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 top left corner, (x_max
,y_max
) are coordinates 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
file.conf
- confidence for the predicted class- (
x_min
,y_min
) - coordinates of the top left bounding box corner (coordinates stored in normalized format, in range [0, 1]) - (
x_max
,y_max
) - coordinates of the bottom right bounding box corner (coordinates stored in normalized format, in range [0, 1])
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:
omz_downloader --name <model_name>
An example of using the Model Converter:
omz_converter --name <model_name>
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
.