# Object detection, classification, and segmentation Source: [https://docs.qualcomm.com/doc/80-70022-50/topic/object-detection-classification-and-segmentation-python-sample-app.html](https://docs.qualcomm.com/doc/80-70022-50/topic/object-detection-classification-and-segmentation-python-sample-app.html) The **gst-filesrc-2detection-classification-segmentation-side-by-side.py** script identifies an object from a scene in a camera stream, overlays the bounding boxes over the detected objects, classifies scenes from the video stream, and produces semantic segmentation for the video. The output is displayed side by side on a screen. Figure : Pipeline for object detection, image classification, and segmentation Qualcomm Open source qtivcomposer filesrc qtdemux h264parse V4l2h264dec filesrc qtdemux h264parse V4l2h264dec filesrc qtdemux h264parse V4l2h264dec filesrc qtdemux h264parse V4l2h264dec tee sink_0 qtimlvconverter qtimlpostprocess qtimetamux qtioverlay qtimltflite tee sink_1 qtimlvconverter qtimlpostprocess qtimetamux qtioverlay qtimltflite tee sink_2 sink_3 sink_4 qtimlvconverter qtimlpostprocess qtimetamux qtioverlay qtimltflite tee qtimlvconverter qtimlpostprocess qtimltflite Waylandsink For information about the plugins used in this pipeline, see [Pipeline flow](https://docs.qualcomm.com/doc/80-70022-50/topic/object-detection-classification-and-segmentation-python-sample-app.html#object-detection-classification-and-segmentation-python-sample-app__section_mty_hyk_bdc). ## Model files Table : Models used for detection and classification | Purpose | LiteRT model | Description | | :--- | :--- | :--- | | Object detection | YOLOX |

  1. Identify the object in a scene from a camera stream.


  2. Overlay the bounding boxes over the detected objects.


| | Image classification | InceptionV3 |

  1. Classify a scene from a camera stream.


  2. Overlay the classification labels on the screen.


| | Image segmentation | Deeplab\_plus\_mobilenet | Produce semantic segmentations for the video file. | ## Run the application on the target device 1. Ensure that you complete the [Prerequisites](https://docs.qualcomm.com/doc/80-70022-50/topic/prerequisites-for-python-sample-applications.html). 2. Run the detection, classification, and segmentation script on the target device: gst-filesrc-2detection-classification-segmentation-side-by-side.pyCopy to clipboard 3. To display the available help options, run the following command: gst-filesrc-2detection-classification-segmentation-side-by-side.py -hCopy to clipboard The following are the input videos: | Input video | Directory | | --- | --- | | Object detection | /etc/media/video.mp4 | | Image classification | /etc/media/video.mp4 | | Image segmentation | /etc/media/video.mp4 | The default file paths in the Python script are as follows: Table : Default directories for model and label files | Model and label files | Directory | | :--- | :--- | | Detection model | /etc/models/yolox\_quantized.tflite | | Detection labels | /etc/labels/yolox.json | | Classification model | /etc/models/inception\_v3\_quantized.tflite | | Classification labels | /etc/labels/classification.json | | Segmentation model | /etc/models/deeplabv3\_plus\_mobilenet\_quantized.tflite | | Segmentation labels | /etc/labels/deeplabv3\_resnet50.json | ## Expected output The four streams can be previewed side by side on a local display. ## Pipeline flow | Process | Description | | --- | --- | | filesrc | Reads the video data from a file. | | qtdemux | Demultiplexes the video data. | | h264parse | Parses the H.264 video. | | [v4l2h264dec](https://docs.qualcomm.com/doc/80-70022-50/topic/v4l2h264dec.html) | Decodes the H.264 video. | | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70022-50/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 object detection, classification, and
    segmentation models use this tensor stream for
    inferencing.




| | **Inferencing** | **Inferencing** | | [qtimltflite](https://docs.qualcomm.com/doc/80-70022-50/topic/qtimltflite.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** | | qtimlpostprocess |

  1. Receives the inference tensors from the object detection
    model.


  2. Converts the inference tensors on its sinkpad 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 for detection models.

    In
    this use case, qtimlpostprocess does the following:


    1. Loads the YOLOv8 submodule.


    2. Produces results as structures of text.


    3. Sends them to the sinkpad of qtimetamux.





| | qtimlpostprocess |

  1. Receives the inference results from a classification 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, qtimlpostprocess 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.





| | qtimlpostprocess |

  1. Receives the inference tensors on its sinkpad.


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


  3. Produces the semantic segmentations for the frame.


  4. Loads the corresponding modules for the segmentation
    models.

    In this use case, qtimlpostprocess does the
    following:


    1. Loads the deeplab-argmax submodule.


    2. Produces video frames with segmentation masks.


    3. Sends them to the sinkpad of qtivcomposer.






| | [qtimetamux](https://docs.qualcomm.com/doc/80-70022-50/topic/qtimetamux.html) |

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


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


  3. Adds bounding boxes as GstVideoRegionOfInterest from data
    sinkpad to GST buffers meta (meta muxing) on its source
    pad.


| | [qtivoverlay](https://docs.qualcomm.com/doc/80-70022-50/topic/qtioverlay.html) |

  1. Receives the multiplexed stream.


  2. Overlays the bounding boxes on the VideoFrame using CL.


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


| | [qtivcomposer](https://docs.qualcomm.com/doc/80-70022-50/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/80-70022-50/topic/waylandsink.html) |

  1. Receives the video in its sinkpad


  2. Submits the video stream to Weston.


  3. Weston renders the video stream on a local display
    device.


| | | | | | | ## Related information - [Object detection](https://docs.qualcomm.com/doc/80-70022-50/topic/gst-ai-object-detection.html) - [Image classification](https://docs.qualcomm.com/doc/80-70022-50/topic/gst-ai-classification.html) - [Image segmentation](https://docs.qualcomm.com/doc/80-70022-50/topic/gst-ai-segmentation.html) **Parent Topic:** [Run Python-based applications](https://docs.qualcomm.com/doc/80-70022-50/topic/python-sample-applications.html) Last Published: Feb 20, 2026 [Previous Topic Camera encode, object detection, and display](https://docs.qualcomm.com/bundle/publicresource/80-70022-50/topics/camera-encode-file-detection-yolov8-overlay-display.md) [Next Topic Parallel inference using Python](https://docs.qualcomm.com/bundle/publicresource/80-70022-50/topics/parallel-inference-using-python.md)