faster_rcnn_inception_resnet_v2_atrous_coco#
Use Case and High-Level Description#
Faster R-CNN with Inception ResNet v2 Atrous version. Used for object detection. For details see the paper.
Specification#
Metric |
Value |
---|---|
Type |
Object detection |
GFlops |
30.687 |
MParams |
13.307 |
Source framework |
TensorFlow* |
Accuracy#
Metric |
Value |
---|---|
coco_precision |
40.69% |
Input#
Original Model#
Image, name: image_tensor
, shape: 1, 600, 1024, 3
, format: B, H, W, C
, where:
B
- batch sizeH
- image heightW
- image widthC
- number of channels
Expected color order: RGB
.
Converted Model#
Image, name:
image_tensor
, shape:1, 600, 1024, 3
, format:B, H, W, C
, where:
B
- batch sizeH
- image heightW
- image widthC
- number of channelsExpected color order:
BGR
.
Information of input image size, name:
image_info
, shape:1, 3
, format:B, C
, where:B
- batch sizeC
- vector of three values in a formatH, W, S
, whereH
is an image height,W
is an image width,S
is an image scale factor (usually 1).
Output#
Original Model#
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.
Converted Model#
The array of summary detection information, name: reshape_do_2d
, 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 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])
Download a Model and Convert it into OpenVINO™ IR Format#
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>
Demo usage#
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
Legal Information#
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
.