# Qualcomm Neural Processing SDK use cases

Source: [https://docs.qualcomm.com/doc/80-70022-50/topic/qualcomm-neural-processing-sdk-use-cases.html](https://docs.qualcomm.com/doc/80-70022-50/topic/qualcomm-neural-processing-sdk-use-cases.html)

Qualcomm Neural Processing SDK (formerly known as Qualcomm Snapdragon Neural
        Processing Engine (SNPE)) is used to run deep neural networks for inference. The use cases
        describe the image classification, object detection, and image segmentation scenarios using
        different ML models.

Note: The deep learning container (DLC) models used in the pipelines
            are available with the Qualcomm Neural Processing SDK release.

Before you run the use cases, complete the preconditions mentioned in [GStreamer command-line use cases](https://docs.qualcomm.com/doc/80-70022-50/topic/gstreamer-application-use-cases.html).

## Related information

[Configure Qualcomm GStreamer plugins](https://docs.qualcomm.com/doc/80-70022-50/topic/qim-sdk-plugins.html)

- **[Image classification and display with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70022-50/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.
- **[Image classification and encode with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-image-classification-and-encode-with-mobilenet-v1.html)**  

The use cases implement the InceptionV3 image classification model with Qualcomm         Neural Processing SDK to classify scenes from a single camera stream and either overlay or         compose the classification labels. The streams are then encoded.
- **[Object detection and display with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-object-detection-and-display-with-mobilenet-v2-ssd.html)**  

The use cases implement a yolox.dlc object detection model with         Qualcomm Neural Processing SDK to identify an object from a camera stream. The use case is         to overlay or compose the bounding boxes over the detected objects, and the display the         results.
- **[Object detection and encode with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-object-detection-and-encode-with-mobilenet-v2-ssd.html)**  

The use cases implement a yolox.dlc object detection model with         Qualcomm Neural Processing SDK to identify an object from a camera stream. The use case is         to overlay or compose the bounding boxes over the detected objects, and then encode the         stream as a H.264 bitstream.
- **[Image segmentation and display with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-image-segmentation-and-display-with-deeplabv3-quantized.html)**  

The use case implements the DeepLab v3 model with the Qualcomm Neural Processing SDK         runtime to identify the semantic segmentations in a scene from a camera stream. The use case         is to compose the semantics and the video stream together using qtivcomposer, and then         display the results.
- **[Image segmentation and encode with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-image-segmentation-and-encode-with-deeplabv3-quantized.html)**  

The use case implements the DeepLab v3 model with the Qualcomm Neural Processing SDK         runtime. The use case is to compose the semantic segmentations and original video stream,         encode this stream, and then multiplex it in an MP4 container.

**Parent Topic:** [Run machine learning use cases](https://docs.qualcomm.com/doc/80-70022-50/topic/machine-learning-use-cases.html)

Last Published: Feb 20, 2026

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