# Qualcomm Neural Processing SDK use cases Source: [https://docs.qualcomm.com/doc/80-70014-50/topic/qualcomm-neural-processing-sdk-use-cases.html](https://docs.qualcomm.com/doc/80-70014-50/topic/qualcomm-neural-processing-sdk-use-cases.html) Qualcomm Neural Processing SDK (formerly known as Qualcomm Snapdragon Neural Processing Engine (SNPE)) is used to execute 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 execute the use cases, complete the preconditions mentioned in [GStreamer command-line use cases](https://docs.qualcomm.com/doc/80-70014-50/topic/gstreamer-application-use-cases.html). - **[Image classification and display with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70014-50/topic/single-camera-stream-with-image-classification-and-display-with-mobilenet-v1.html)** The use cases use 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/80-70014-50/topic/single-camera-stream-with-image-classification-and-encode-with-mobilenet-v1.html)** The use cases use the Inceptionv3 Image Classification model with Qualcomm Neural Processing SDK to classify scenes a single camera stream and either overlay or compose the classification labels, and then encode the stream. - **[Object detection and display with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70014-50/topic/single-camera-stream-with-object-detection-and-display-with-mobilenet-v2-ssd.html)** The use cases use a yolonas.dlc object detection model with Qualcomm Neural Processing SDK to identify an object from a camera stream, overlay or compose the bounding boxes over the detected objects, and then display the results. - **[Object detection and encode with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70014-50/topic/single-camera-stream-with-object-detection-and-encode-with-mobilenet-v2-ssd.html)** The use cases use a yolonas.dlc object detection model with Neural Processing SDK to identify an object from a camera stream, 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/80-70014-50/topic/single-camera-stream-with-image-segmentation-and-display-with-deeplabv3-quantized.html)** The use case uses the DeepLab v3 model with Qualcomm Neural Processing SDK runtime to identify the semantic segmentations in a scene from a video stream coming through a camera source, compose the semantics and original video stream together using qtivcomposer, and then display the results. - **[Image segmentation and encode with Neural Processing SDK](https://docs.qualcomm.com/doc/80-70014-50/topic/single-camera-stream-with-image-segmentation-and-encode-with-deeplabv3-quantized.html)** The use case uses the DeepLab v3 model with Qualcomm Neural Processing SDK runtime to compose the semantic segmentations and original video stream, encode this stream, and then multiplex it in a MP4 container. **Parent Topic:** [Machine learning use cases](https://docs.qualcomm.com/doc/80-70014-50/topic/machine-learning-use-cases.html) **Related Resources** - [Qualcomm GST plugins](https://docs.qualcomm.com/doc/80-70014-50/topic/qim-sdk-plugins.html) Last Published: Oct 27, 2025 [Previous Topic Pose estimation and encode with TFLite](https://docs.qualcomm.com/bundle/publicresource/80-70014-50/topics/single-camera-stream-with-pose-estimation-and-encode.md) [Next Topic Image classification and display with Neural Processing SDK](https://docs.qualcomm.com/bundle/publicresource/80-70014-50/topics/single-camera-stream-with-image-classification-and-display-with-mobilenet-v1.md)