-# Image Classification Sample
+# Image Classification C++ Sample
-This topic demonstrates how to run the Image Classification sample application, which performs
+This topic demonstrates how to run the Image Classification sample application, which performs
inference using image classification networks such as AlexNet and GoogLeNet.
-## Running
+> **NOTE:** This topic describes usage of C++ implementation of the Image Classification Sample. For the Python* implementation, refer to [Image Classification Python* Sample](./inference-engine/ie_bridges/python/sample/classification_sample/README.md).
+
+## 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 the application with the <code>-h</code> option yields the following usage message:
+## Running
+Running the application with the `-h` option yields the following usage message:
```sh
./classification_sample -h
-InferenceEngine:
+InferenceEngine:
API version ............ <version>
Build .................. <number>
-i "<path1>" "<path2>" Required. Path to a folder with images or path to an image files: a .ubyte file for LeNet
and a .bmp file for the other networks.
-m "<path>" Required. Path to an .xml file with a trained model.
- -l "<absolute_path>" Optional. Absolute path to library with MKL-DNN (CPU) custom layers (*.so).
+ -l "<absolute_path>" Required for CPU custom layers. Absolute path to a shared library with the kernels implementations.
Or
- -c "<absolute_path>" Optional. Absolute path to clDNN (GPU) custom layers config (*.xml).
+ -c "<absolute_path>" Required for GPU custom kernels. Absolute path to the .xml file with the kernels descriptions.
-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
- -nt "<integer>" Number of top results (default 10)
- -ni "<integer>" Number of iterations (default 1)
+ -d "<device>" 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
+ -nt "<integer>" Number of top results. Default value is 10
+ -ni "<integer>" Number of iterations. Default value is 1
-pc Enables per-layer performance report
-p_msg Enables messages from a plugin
Running the application with the empty list of options yields the usage message given above.
-To run the sample you can use AlexNet and GoogLeNet models that can be downloaded with the OpenVINO [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader) or other image classification models.
+To run the sample, you can use AlexNet and GoogLeNet or other public or pre-trained image classification 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/).
-> **IMPORTANT**: To run the sample, the model should be first converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
+> **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 perform inference of an AlexNet model (previously converted to the Inference Engine format) on CPU, use the following command:
+For example, to perform inference of an AlexNet model on CPU, use the following command:
```sh
./classification_sample -i <path_to_image>/cat.bmp -m <path_to_model>/alexnet_fp32.xml
```
-### Outputs
+## Demo Output
+
+By default the application outputs top-10 inference results.
+Add the `-nt` option to the previous command to modify the number of top output results.
-By default the application outputs top-10 inference results.
-Add the <code>-nt</code> option to the previous command to modify the number of top output results.
-<br>For example, to get the top-5 results on Intel® HD Graphics, use the following commands:
+For example, to get the top-5 results on GPU, use the following commands:
```sh
./classification_sample -i <path_to_image>/cat.bmp -m <path_to_model>/alexnet_fp32.xml -nt 5 -d GPU
```
-### 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 tool](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader)
\ No newline at end of file
+* [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