The Pelee is a Real-Time Object Detection System on Mobile Devices based on Single Shot Detection approach. The model is implemented using the Caffe* framework and trained on Common Objects in Context (COCO) dataset. For details about this model, check out the repository.
Metric | Value |
---|---|
Type | Detection |
GFLOPs | 1,290 |
MParams | 5.98 |
Source framework | Caffe* |
Metric | Value |
---|---|
coco_precision | 21.9761% |
See here.
Image, name - data
, shape - 1, 3, 304, 304
, format is B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
.
Mean values - [103.94, 116.78, 123.68], Scale - 58.8235.
Image, name - data
, shape - 1, 3, 304, 304
, format is B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
.
The array of detection summary info, name - detection_out
, 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 ID in range [1, 80], mapping to class names provided in<omz_dir>/data/dataset_classes/coco_80cl.txt
fileconf
- confidence for the predicted class- (
x_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, 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 ID in range [1, 80], mapping to class names provided in<omz_dir>/data/dataset_classes/coco_80cl_bkgr.txt
fileconf
- confidence for the predicted class- (
x_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:
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.txt
.
[*] Other names and brands may be claimed as the property of others.