# Image classification and display with Neural Processing SDK Source: [https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-image-classification-and-display-with-mobilenet-v1.html](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-image-classification-and-display-with-mobilenet-v1.html) The use cases implement an Inceptionv3 model with Qualcomm Neural Processing SDK to classify scenes, either overlay or compose the classification labels, and then display the results. You can use any publicly available classification model with TensorFlow and convert it to the `.dlc` format as described in [TensorFlow Model Conversion](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-2/model_conv_tensorflow.html). ## Use qtivoverlay plugin to apply classification overlay Run the use case on the target device: gst-launch-1.0 -e --gst-debug=2 \ qtiqmmfsrc name=camsrc ! video/x-raw,format=NV12_Q08C,width=1280,height=720,framerate=30/1 ! queue ! tee name=split \ split. ! queue ! qtimetamux name=metamux ! queue ! qtivoverlay ! queue ! waylandsink fullscreen=true sync=false \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/etc/models/inceptionv3.dlc ! queue ! \ qtimlvclassification threshold=40.0 results=2 module=mobilenet labels=/etc/labels/classification.labels ! text/x-raw ! queue ! metamux.Copy to clipboard To stop the use case, use CTRL + C. The following figure shows the flow of the use case execution: - Classify scenes from a video stream coming through a camera source. - Overlay classification labels using overlaylib. - Display the results on a local display. Figure : Pipeline for classification overlay The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70020-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


  3. Converts the video stream to a tensor stream on its source
    pad.

    The classification model uses this tensor stream
    for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlsnpe.html) |

  1. Loads the model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Runs the inference and produces a tensor stream with the
    inference results on its source pad.


| | **Postprocessing** | **Postprocessing** | | [qtimlvclassification](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlvclassification.html) |

  1. Receives the inference tensors from the model on its
    sinkpad.


  2. Converts the tensors into formats such as video or text that
    the multimedia plugins can process later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules of the classification
    models.

    In this use case, qtimlvclassification does the
    following:


    1. Loads the submodule of the model.


    2. Produces results as structures of text.


    3. Sends them to the sinkpad of qtimetamux.





| | [qtimetamux](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimetamux.html) |

  1. Receives the video stream and text stream with
    classification results corresponding to the video stream on
    its sinkpads.


  2. Produces GST buffers with the contents of video stream on
    its sink pad.


  3. Adds classification result from data sinkpad to GST buffer
    meta (meta muxing) on its source pad.


| | [qtivoverlay](https://docs.qualcomm.com/doc/80-70020-50/topic/qtioverlay.html) |

  1. Receives the multiplexed stream.


  2. Overlays the classification labels on the VideoFrame using
    CL.


  3. Produces GST buffers with overlays in its source pad.


| | **Output** | **Output** | | [Waylandsink](https://docs.qualcomm.com/doc/80-70020-50/topic/waylandsink.html) |

  1. Receives the video stream on its sinkpad.


  2. Submits the video stream to Weston.


  3. Weston renders the video stream and possible classifications
    generated for that scene on a local display device.


| ## Use qtivcomposer to mix original frame with classification mask Run the use case on the target device: gst-launch-1.0 -e --gst-debug=2 qtiqmmfsrc name=camsrc ! video/x-raw,format=NV12_Q08C,width=1280,height=720,framerate=30/1 ! queue ! \ tee name=split split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<320, 320>" ! queue ! waylandsink fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/etc/models/inceptionv3.dlc ! queue ! \ qtimlvclassification threshold=40.0 results=2 module=mobilenet labels=/etc/labels/classification.labels ! video/x-raw,format=BGRA,width=640,height=360 ! queue ! mixer.Copy to clipboard To stop the use case, use CTRL + C. The following figure shows the flow of the use case execution: - Classify scenes from a video stream coming through a camera source. - Compose classification labels and video stream together using qtivcomposer. - Display the results to a local display. Figure : Pipeline for classification using qtivcomposer The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70020-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


  3. Converts the video stream to a tensor stream on its source
    pad.

    The classification model uses this tensor stream
    for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlsnpe.html) |

  1. Loads the model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Runs the inference and produces a tensor stream with the
    inference results on its source pad.


| | **Postprocessing** | **Postprocessing** | | [qtimlvclassification](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlvclassification.html) |

  1. Receives the inference results from the model on its
    sinkpad.


  2. Converts the inference tensors into formats like video or
    text that the multimedia plugins can process later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules for the classification
    models.

    In this use case, qtimlvclassification does the
    following:


    1. Loads the submodule of the model.


    2. Produces results as video frames with classification
      labels.


    3. Sends them to the sinkpad of qtivcomposer.





| | [qtivcomposer](https://docs.qualcomm.com/doc/80-70020-50/topic/qtivcomposer.html) |

  1. Receives the original video stream with classification
    results on its sinkpads.


  2. On its sourcepad, produces GST buffers with contents
    composed of video streams from its sinkpads.


| | **Output** | **Output** | | [Waylandsink](https://docs.qualcomm.com/doc/80-70020-50/topic/waylandsink.html) |

  1. Receives the video in its sinkpad


  2. Submits the video stream to Weston.


  3. Weston renders the video stream and possible classifications
    generated for that scene on a local display device.


| **Parent Topic:** [Qualcomm Neural Processing SDK use cases](https://docs.qualcomm.com/doc/80-70020-50/topic/qualcomm-neural-processing-sdk-use-cases.html) Last Published: Jan 30, 2026 [Previous Topic Qualcomm Neural Processing SDK use cases](https://docs.qualcomm.com/bundle/publicresource/80-70020-50/topics/qualcomm-neural-processing-sdk-use-cases.md) [Next Topic Image classification and encode with Neural Processing SDK](https://docs.qualcomm.com/bundle/publicresource/80-70020-50/topics/single-camera-stream-with-image-classification-and-encode-with-mobilenet-v1.md)