-# Object Detection Sample SSD
+# Object Detection C++ Sample SSD
-This topic demonstrates how to run the Object Detection sample application, which does inference using object detection
+This topic demonstrates how to run the Object Detection sample application, which does inference using object detection
networks like SSD-VGG on Intel® Processors and Intel® HD Graphics.
+## How It Works
+
+Upon the start-up the sample 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](./docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
+
## Running
Running the application with the <code>-h</code> option yields the following usage message:
```sh
./object_detection_sample_ssd -h
-InferenceEngine:
+InferenceEngine:
API version ............ <version>
Build .................. <number>
-h Print a usage message.
-i "<path>" Required. Path to an .bmp image.
-m "<path>" Required. Path to an .xml file with a trained model.
- -l "<absolute_path>" Required for MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the kernels impl.
+ -l "<absolute_path>" Required for CPU custom layers. Absolute path to a shared library with the kernel implementations.
Or
- -c "<absolute_path>" Required for clDNN (GPU)-targeted custom kernels. Absolute path to the xml file with the kernels desc.
- -pp "<path>" Path to a plugin folder.
- -d "<device>" Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. Sample will look for a suitable plugin for device specified
- -pc Enables per-layer performance report
- -ni "<integer>" Number of iterations (default 1)
- -p_msg Enables messages from a plugin
+ -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. Sample will look for a suitable plugin for device specified
+ -pc Optional. Enables per-layer performance report
+ -ni "<integer>" Optional. Number of iterations. Default value is 1
+ -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 sample, you can use a set of pre-trained and optimized models delivered with the package or a Caffe* public model.
+To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
-> **NOTE**: A public model should be converted to the Inference Engine format (`.xml` + `.bin`) using the Model Optimizer tool. For Model Optimizer documentation, see https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer.
+> **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
For example, to do inference on a CPU with the OpenVINO™ toolkit person detection SSD models, run one of the following commands:
```sh
-./object_detection_sample_ssd -i <path_to_image>/inputImage.bmp -m <INSTAL_DIR>/deployment_tools/intel_models/person-detection-retail-0013/FP32/person-detection-retail-0013.xml -d CPU
+./object_detection_sample_ssd -i <path_to_image>/inputImage.bmp -m <path_to_model>person-detection-retail-0013.xml -d CPU
```
or
```sh
-./object_detection_sample_ssd -i <path_to_image>/inputImage.jpg -m <INSTALL_DIR>/deployment_tools/intel_models/person-detection-retail-0002/FP32/person-detection-retail-0002.xml -d CPU
+./object_detection_sample_ssd -i <path_to_image>/inputImage.jpg -m <path_to_model>person-detection-retail-0002.xml -d CPU
```
-> **NOTE**: Before running the sample with another trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
+## Sample Output
-### Outputs
-
-The application outputs an image (<code>out_0.bmp</code>) 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
+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.
-### How it works
-
-Upon the start-up the sample 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.
-## See Also
+## See Also
* [Using Inference Engine Samples](./docs/IE_DG/Samples_Overview.md)
+* [Model Optimizer](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
+* [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader)
projects / platform / upstream / dldt.git / blobdiff