The mobilenet-ssd
model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. This model is implemented using the Caffe* framework. For details about this model, check out the repository.
The model input is a blob that consists of a single image of 1x3x300x300 in BGR order, also like the densenet-121
model. The BGR mean values need to be subtracted as follows: [127.5, 127.5, 127.5] before passing the image blob into the network. In addition, values must be divided by 0.007843.
The model output is a typical vector containing the tracked object data, as previously described.
Metric | Value |
---|---|
Type | Detection |
GFLOPs | 2.316 |
MParams | 5.783 |
Source framework | Caffe* |
Metric | Value |
---|---|
mAP | 79.8377% |
Image, name - prob
, shape - 1,3,300,300
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is BGR
. Mean values - [127.5, 127.5, 127.5], scale value - 127.5.
Image, name - prob
, shape - 1,3,300,300
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is BGR
The array of detection summary info, name - detection_out
, shape - 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 (1..20 - PASCAL VOC defined class ids). Mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt
file.conf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])x_max
, y_max
) - coordinates of the bottom right bounding box corner (coordinates are in normalized format, in range [0, 1])The array of detection summary info, name - detection_out
, shape - 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 (1..20 - PASCAL VOC defined class ids). Mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt
file.conf
- confidence for the predicted classx_min
, y_min
) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])x_max
, y_max
) - coordinates of the bottom right bounding box corner (coordinates are 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:
An example of using the Model Converter:
The original model is distributed under the following license: