# LiteRT use cases

Source: [https://docs.qualcomm.com/doc/80-70020-50/topic/tensorflow-lite-use-cases.html](https://docs.qualcomm.com/doc/80-70020-50/topic/tensorflow-lite-use-cases.html)

LiteRT is a set of tools that allows on-device machine learning. You can  run your
        models on mobile, embedded, and edge devices. LiteRT use cases allow you to run use cases
        for image classification, object detection, image segmentation, and pose
        estimation.

Before you run the use cases, complete the preconditions mentioned in [GStreamer command-line use cases](https://docs.qualcomm.com/doc/80-70020-50/topic/gstreamer-application-use-cases.html).

## Related information

- [https://ai.google.dev/edge/litert](https://ai.google.dev/edge/litert)
- [Configure Qualcomm GStreamer plugins](https://docs.qualcomm.com/doc/80-70020-50/topic/qim-sdk-plugins.html)
- [Get the model constants](https://docs.qualcomm.com/bundle/publicresource/topics/80-70020-15B/integrate-ai-hub-models.html#get-the-model-constants)

- **[Image classification and display with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-image-classification-and-display-with-litert.html)**  

The use cases use the Inceptionv3 LiteRT model to classify scenes from a single         camera stream and either overlay or compose the classification labels.
- **[Image classification and encode with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-image-classification-and-encode.html)**  

The use cases use the InceptionV3 LiteRT model to classify scenes from a single         camera stream and either overlay or compose the classification labels, and then encode the         stream.
- **[Audio classification decode and display with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/audio-classification-with-litert.html)**  

The use cases implement the YAMNet LiteRT model to classify and decode audio samples         from a microphone and a file source.
- **[Object detection and display with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-object-detection-and-display.html)**  

The use cases use a YOLOv5 LiteRT model to identify the object in a scene. The use         case is to either overlay or compose the bounding boxes over the detected objects, and then         display the results.
- **[Object detection and encode with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-object-detection-and-encode.html)**  

The use cases use a YOLOv5 LiteRT model to identify the object in a scene. The use         case is to either overlay or compose the bounding boxes over the detected objects, and then         encode this stream as an H.264 bitstream.
- **[Image segmentation and display with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-image-segmentation-and-display.html)**  

The use case implements the `deeplabv3_resnet50` LiteRT model to         identify semantic segmentations in a scene from a camera stream. The use case is to compose         the semantics and original video stream using qtivcomposer, and then display the         results.
- **[Image segmentation and encode with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-image-segmentation-and-encode.html)**  

The use case implements the `deeplabv3_resnet50` LiteRT model to         compose the semantic segmentations and original video stream, encode this stream, and then         multiplex it in an MP4 container.
- **[Pose estimation and display with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-pose-estimation-and-display.html)**  

The use cases implement the PoseNet LiteRT model to process a single camera stream         with pose estimation.
- **[Pose estimation and encode with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/single-camera-stream-with-pose-estimation-and-encode.html)**  

The use cases implement the PoseNet LiteRT model to process a single camera stream         with pose estimation and encode the stream as an H.264 bitstream.
- **[Video super resolution and display with LiteRT](https://docs.qualcomm.com/doc/80-70020-50/topic/video-super-resolution-and-display-with-litert.html)**  

Video super resolution (VSR) is supported on Qualcomm AI Hub quantized INT8 models         with 128 ×128 input resolution and 512 × 512 output resolution.
- **[Single stream from camera to RTSP with ML detection](https://docs.qualcomm.com/doc/80-70020-50/topic/single-stream-from-camera-to-rtsp-with-ml-detection.html)**  

Play a stream from the camera through RTSP on a media player (such as         VLC).

**Parent Topic:** [Run machine learning use cases](https://docs.qualcomm.com/doc/80-70020-50/topic/machine-learning-use-cases.html)

Last Published: Jan 30, 2026

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