Object Detection Faster R-CNN C++ Demo

This topic demonstrates how to run the Object Detection demo application, which does inference using object detection networks like Faster R-CNN on Intel® Processors and Intel® HD Graphics.

Downloading and Converting Caffe* Model

VGG16-Faster-RCNN is a public CNN that can be easily obtained from GitHub:

  1. Download test.prototxt from https://raw.githubusercontent.com/rbgirshick/py-faster-rcnn/master/models/pascal_voc/VGG16/faster_rcnn_end2end/test.prototxt
  2. Download pre-trained models from https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0
  3. Unzip the archive. You will need the VGG16_faster_rcnn_final.caffemodel file.

To convert the source model, run the Model Optimizer. You can use the following command to convert the source model:

python3 ${MO_ROOT_PATH}/mo_caffe.py --input_model <path_to_model>/VGG16_faster_rcnn_final.caffemodel --input_proto <path_to_model>/deploy.prototxt

For documentation on how to convert Caffe models, refer to Converting a Caffe Model.

How It Works

Upon the start-up, the demo application reads command line parameters and loads a network and an image to the Inference Engine plugin. When inference is done, the application creates an output image and outputs data to the standard output stream.

NOTE: By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument specified. For more information about the argument, refer to When to Specify Input Shapes section of Converting a Model Using General Conversion Parameters.


Running the application with the -h option yields the following usage message:

./object_detection_demo -h
API version ............ <version>
Build .................. <number>
object_detection_demo [OPTION]
-h Print a usage message.
-i "<path>" Required. Path to a .bmp image.
-m "<path>" Required. Path to an .xml file with a trained model.
-l "<absolute_path>" Required for CPU custom layers. Absolute path to a shared library with the kernel implementations.
-c "<absolute_path>" Required for GPU custom kernels. Absolute path to the .xml file with the kernel descriptions.
-pp "<path>" Optional. Path to a plugin folder.
-d "<device>" Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is acceptable. The demo will look for a suitable plugin for a specified device.
-pc Optional. Enables per-layer performance report
-ni "<integer>" Optional. Number of iterations. Default value is 1
-bbox_name "<string>" Optional. The name of output box prediction layer. Default value is "bbox_pred"
-proposal_name "<string>" Optional. The name of output proposal layer. Default value is "proposal"
-prob_name "<string>" Optional. The name of output probability layer. Default value is "cls_prob"
-p_msg Optional. Enables messages from a plugin

Running the application with the empty list of options yields the usage message given above and an error message.

To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader or go to https://download.01.org/opencv/.

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

You can use the following command to do inference on CPU on an image using a trained Faster R-CNN network:

./object_detection_demo -i <path_to_image>/inputImage.bmp -m <path_to_model>/faster-rcnn.xml -d CPU

Demo Output

The application outputs an image (out_0.bmp) with detected objects enclosed in rectangles. It outputs the list of classes of the detected objects along with the respective confidence values and the coordinates of the rectangles to the standard output stream.

See Also