# TensorFlow Lite use cases

Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/tensorflow-lite-use-cases.html](https://docs.qualcomm.com/doc/80-70015-50/topic/tensorflow-lite-use-cases.html)

TensorFlow Lite is a set of tools that enables on-device machine learning by helping
        developers run their models on mobile, embedded, and edge devices. TensorFlow lite use cases
        enable 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/80-70015-50/topic/gstreamer-application-use-cases.html).

- **[Image classification and display with TFLite](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-image-classification-and-display.html)**  

The use cases use the Inceptionv3 TFLite model to classify scenes from a single         camera stream and either overlay or compose the classification labels.
- **[Image classification and encode with TFLite](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-image-classification-and-encode.html)**  

The use cases use the InceptionV3 TFLite model to classify scenes from a single         camera stream and either overlay or compose the classification labels, and then encode the         stream.
- **[Object detection and display with TFLite](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-object-detection-and-display.html)**  

The use cases use a YOLOv5 TFLite model to identify the object in a scene and either         overlay or compose the bounding boxes over the detected objects, and then display the         results.
- **[Object detection and encode with TFLite](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-object-detection-and-encode.html)**  

The use cases use a YOLOv5 TFLite model to identify the object in a scene and either         overlay or compose the bounding boxes over the detected objects, and then encode this stream         as a H.264 bitstream.
- **[Image segmentation and display with TFLite](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-image-segmentation-and-display.html)**  

The use case uses the `deeplabv3_resnet50` TFLite model to identify         semantic segmentations in a scene from a camera stream, compose the semantics and original         video stream using qtivcomposer, and then display the results.
- **[Image segmentation and encode with TFLite](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-image-segmentation-and-encode.html)**  

The use case uses the `deeplabv3_resnet50` TFLite 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 TFLite](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-pose-estimation-and-display.html)**  

The use cases use the PoseNet TFLite model to process a single camera stream with         pose estimation.
- **[Pose estimation and encode with TFLite](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-pose-estimation-and-encode.html)**  

The use cases use the PoseNet TFLite model to process a single camera stream with         pose estimation and encode the stream as an H.264 bitstream.

**Parent Topic:** [Machine learning use cases](https://docs.qualcomm.com/doc/80-70015-50/topic/machine-learning-use-cases.html)

**Related Resources**  

- [Qualcomm GStreamer plugins](https://docs.qualcomm.com/doc/80-70015-50/topic/qim-sdk-plugins.html)

**Related Information**  

- [https://www.tensorflow.org/lite/guide](https://www.tensorflow.org/lite/guide)

Last Published: Oct 27, 2025

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