# AI/ML sample applications Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/ai-ml-sample-applications.html](https://docs.qualcomm.com/doc/80-70015-50/topic/ai-ml-sample-applications.html) Run custom use cases that show how to use the Qualcomm Linux AI/ML features using Qualcomm Neural Processing SDK and TensorFlow Lite (TFLite) models. Before you run the AI sample applications, ensure that the model and label files are available on the device. ## Download model and label files for Qualcomm Neural Processing SDK To download and push the model and label files for QCS6490 and QCS9075, do the following on the Linux host: 1. Download from GitHub and extract the files: wget https://github.com/quic/sample-apps-for-qualcomm-linux/releases/download/GA1.2-rel/GA1.2-rel.zipCopy to clipboard unzip GA1.2-rel.zipCopy to clipboard 2. Push the model and label files to the device: 1. Enable SSH to log into the target device. For instructions, see [Use SSH](https://docs.qualcomm.com/bundle/publicresource/topics/80-70015-254/how_to.html#use-ssh). 2. Push the files: - **QCS6490** scp -r GA1.2-rel/QCS6490/* root@:/opt/Copy to clipboard - **QCS9075** scp -r GA1.2-rel/QCS9075/* root@:/opt/Copy to clipboard ## Download model and label files for TFLite from AI Hub 1. Download the models from [https://aihub.qualcomm.com/iot/models](https://aihub.qualcomm.com/iot/models). For information on updating the q\_offset and q\_scale constants of the quantized model, see [AI developer workflow](https://docs.qualcomm.com/bundle/publicresource/topics/80-70015-15B/integrate-aihub-model.html). 2. Download the YOLOv8 and YOLO-NAS TFLite models or create your model using AI Hub APIs. | Download models | From... | | :--- | :--- | | Download the models from Hugging Face. | | | Install AI Hub and create your own TFLite model using the
AI Hub APIs. |


Note: Install AI Model Efficiency
Toolkit (AIMET) to generate the quantized model. For
instructions, see [AIMET
Installation](https://quic.github.io/aimet-pages/releases/latest/install/index.html). | | Generate your batch TFLite model using the AI Hub
APIs. |


The gst-ai-multistream-batch-inference application uses
the `batch-size=4` model. To change the
batch size of the model, update the
`export` command:




python -m qai_hub_models.models..export --batch-size 4 --device "QCS6490 (Proxy)"Copy to clipboard | 3. Push the model and label files to the device: 1. Enable SSH to log into the host device. For instructions, see [Use SSH](https://docs.qualcomm.com/bundle/publicresource/topics/80-70015-254/how_to.html#use-ssh). 2. Push the model files: scp mobilenet_v2_quantized.tflite root@:/opt/Copy to clipboard 3. Push the labels to the device: wget https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txtCopy to clipboard scp imagenet_labels.txt root@:/opt/Copy to clipboard Note: Ensure that you push the label file that matches the model. To enable the Performance mode when using the TFLite runtime, update the HTP external delegate parameter in the application source code: external-delegate-options="QNNExternalDelegate,backend_type=htp,htp_device_id=(string)0,htp_performance_mode=(string)2,htp_precision=(string)1;"Copy to clipboard Note: The following sample applications can be built using the default models provided by Qualcomm. If you want to *Bring Your Own Model*, see [AI Developer Workflow](https://docs.qualcomm.com/bundle/publicresource/topics/80-70014-15B). - **[Classification](https://docs.qualcomm.com/doc/80-70015-50/topic/gst-ai-classification.html)** The **gst-ai-classification** application enables you to identify the subject in an image. The use cases use either the Qualcomm Neural Processing SDK runtime or TensorFlow Lite (TFLite) runtime. - **[Object detection](https://docs.qualcomm.com/doc/80-70015-50/topic/gst-ai-object-detection.html)** The **gst-ai-object-detection** application enables you to detect objects within images and videos. The use cases show the execution of [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv8](https://github.com/ultralytics/ultralytics), and [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) using the Qualcomm Neural Processing SDK runtime, and YOLOv5 and YOLOv8 using TFLite runtime. - **[Pose detection](https://docs.qualcomm.com/doc/80-70015-50/topic/gst-ai-pose-detection.html)** The **gst-ai-pose-detection** application enables you to detect the body pose of the subject in an image or video. The use cases use an input stream from a camera, file, or an RTSP source, use TFLite for pose detection, and display the results on the screen. - **[Image segmentation](https://docs.qualcomm.com/doc/80-70015-50/topic/gst-ai-segmentation.html)** The **gst-ai-segmentation** application enables you to divide an image into different and meaningful parts or segments and assign a label to each homogenous segment based on the similarity of the attributes. The application shows how to use Qualcomm Neural Processing SDK runtime and TFLite runtime for image segmentation. - **[Parallel AI fusion](https://docs.qualcomm.com/doc/80-70015-50/topic/gst-ai-parallel-inference.html)** The **gst-ai-parallel-inference** application enables you to perform object detection, object classification, pose detection, and image segmentation on an input stream from different sources such as a camera, a file, or an RTSP network. The use cases use the Qualcomm Neural Processing SDK runtime for object detection and image segmentation, and TFLite runtime for classification and pose detection. - **[Multi-input AI inferencing](https://docs.qualcomm.com/doc/80-70015-50/topic/gst-ai-multi-input-output-object-detection.html)** The **gst-ai-multi-input-output-object-detection** application enables you to perform object detection on multiple video streams from different sources such as a camera, a file, or over a network such as RTSP. - **[Daisy chain detection and classification](https://docs.qualcomm.com/doc/80-70015-50/topic/daisy-chain-detection-and-classification.html)** The **gst-ai-daisychain-detection-classification** application enables you to perform cascaded object detection and classification with a camera, file source, or RTSP stream. The use case involves detecting objects and classifying the detected objects. - **[Daisy chain detection and pose estimation](https://docs.qualcomm.com/doc/80-70015-50/topic/daisy-chain-detection-and-pose-detection.html)** The **gst-ai-daisychain-detection-pose** application enables you to perform cascaded object detection and pose detection with a camera, file source, or an RTSP stream. The use cases involve detecting objects and estimating the body poses of the subject in an image or a video. - **[Mono depth from video](https://docs.qualcomm.com/doc/80-70015-50/topic/mono-depth-from-video.html)** The **gst-ai-monodepth** application enables you to infer depth of a source feed from a live camera stream, file, or an RTSP stream. - **[Video super resolution](https://docs.qualcomm.com/doc/80-70015-50/topic/video-super-resolution.html)** The **gst-ai-superresolution** application enables you to generate high resolution video frames from low-resolution input. - **[Multistream inference](https://docs.qualcomm.com/doc/80-70015-50/topic/multistream-inference.html)** The **gst-ai-multistream-inference** application shows AI inference (object detection and classification) on up to 16 input streams coming from camera, file, or RTSP stream. - **[Multistream batch inference](https://docs.qualcomm.com/doc/80-70015-50/topic/multistream-batch-inference.html)** The **gst-ai-multistream-batch-inference** application shows batched AI inference (object detection and segmentation) on up to 24 input streams from video files. - **[Image segmentation using Python](https://docs.qualcomm.com/doc/80-70015-50/topic/image-segmentation-using-python.html)** The application enables you to perform image segmentation using the Qualcomm Neural Processing SDK with Python bindings, all from within a docker container. **Parent Topic:** [Sample applications](https://docs.qualcomm.com/doc/80-70015-50/topic/example-applications.html) **Related Resources** - [Qualcomm GStreamer plugins](https://docs.qualcomm.com/doc/80-70015-50/topic/qim-sdk-plugins.html) Last Published: Oct 27, 2025 [Previous Topic Sample applications](https://docs.qualcomm.com/bundle/publicresource/80-70015-50/topics/example-applications.md) [Next Topic Classification](https://docs.qualcomm.com/bundle/publicresource/80-70015-50/topics/gst-ai-classification.md)