# Run AI/ML sample applications Use Qualcomm AI/ML features with Qualcomm AI Runtime SDK (Qualcomm Neural Processing SDK and Qualcomm^®^ AI Engine direct), and LiteRT (formerly TFLite) models. - **[Prerequisites](https://docs.qualcomm.com/doc/80-70020-50/topic/download-model-and-label-files.html)** Complete these preconditions before running the AI/ML sample applications. - **[Image classification](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-classification.html)** The **gst-ai-classification** application allows you to identify the subject in an image. The use cases are implemented using the Qualcomm Neural Processing SDK, LiteRT, or Qualcomm AI Engine direct models. - **[Object detection](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-object-detection.html)** The **gst-ai-object-detection** application allows 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 [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/README.md) using the Qualcomm Neural Processing SDK runtime, YOLOv8 using Qualcomm AI Engine direct, and YOLOv5 and YOLOv8 using LiteRT. - **[Pose detection](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-pose-detection.html)** The **gst-ai-pose-detection** application allows you to detect the body pose of the subject in an image or video. The use case processes input streams from a camera, file, or an RTSP source, use LiteRT and Qualcomm AI Engine direct models for pose detection, and display the results on the screen. - **[Image segmentation](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-segmentation.html)** The **gst-ai-segmentation** application allows 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 uses Qualcomm Neural Processing SDK runtime, Qualcomm AI Engine direct runtime, and LiteRT for image segmentation. - **[Image segmentation using Python with container](https://docs.qualcomm.com/doc/80-70020-50/topic/image-segmentation-using-python.html)** The application allows you to perform image segmentation using the Qualcomm Neural Processing SDK with Python bindings, all from within a Docker container. - **[Parallel inferencing](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-parallel-inference.html)** The **gst-ai-parallel-inference** application allows 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 implement the LiteRT models for object detection, image segmentation, classification, and pose detection. - **[Multi input/output object detection](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-multi-input-output-object-detection.html)** The **gst-ai-multi-input-output-object-detection** application allows you to perform objection detection on video streams from various sources such as a camera, a file, or over a network such as RTSP. - **[Daisy chain detection and classification](https://docs.qualcomm.com/doc/80-70020-50/topic/daisy-chain-detection-and-classification.html)** The **gst-ai-daisychain-detection-classification** application allows 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/80-70020-50/topic/daisy-chain-detection-and-pose-detection.html)** The **gst-ai-daisychain-detection-pose** application allows 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. - **[Monodepth from video](https://docs.qualcomm.com/doc/80-70020-50/topic/mono-depth-from-video.html)** The **gst-ai-monodepth** application allows 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/80-70020-50/topic/video-super-resolution.html)** The **gst-ai-superresolution** application allows you to generate high resolution video frames from low-resolution input. - **[Multistream inference](https://docs.qualcomm.com/doc/80-70020-50/topic/multistream-inference.html)** The **gst-ai-multistream-inference** application shows AI inference (object detection and classification) on up to 32 input streams coming from camera, file, or RTSP stream. - **[Multistream batch inference](https://docs.qualcomm.com/doc/80-70020-50/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. - **[AI smart codec](https://docs.qualcomm.com/doc/80-70020-50/topic/ai-smart-codec.html)** The **gst-ai-smartcodec-example** application reduces network bandwidth and storage resources for input from a camera or a file source. - **[Face detection](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-face-detection.html)** The **gst-ai-face-detection** application collects the live video input from a camera, file, or an RTSP stream and uses the Qualcomm AI Engine direct and LiteRT face detection models to produce a preview with the overlaid AI model output on the HDMI display. - **[Face recognition](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-face-recognition.html)** The **gst-ai-face-recognition** application collects the live video input from a camera or an RTSP stream and shares this input for face detection, facial landmarking, and face recognition. It uses the face\_det\_quantized models for face detection, `facemap_3dmm_quantized` model for facial landmarking, and `face_attrib_net_quantized` model for face recognition labels. - **[Audio classification](https://docs.qualcomm.com/doc/80-70020-50/topic/audio-classification.html)** The **gst-ai-audio-classification** application shows audio classification using input from either a file source or a microphone. It displays both the classification results and a video preview. - **[Metadata parsing](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-metadata-parser.html)** The **gst-ai-metadata-parser-example** application receives the live video stream input from camera, file, or RTSP source, and passes the stream to the YOLO models for object detection and preview. The overlaid AI model output, including labels and bounding boxes, is displayed on an HDMI display. The extracted metadata is logged to the console and used to count the number of humans in the frame. - **[AI USB camera](https://docs.qualcomm.com/doc/80-70020-50/topic/ai-usb-camera.html)** The **gst-ai-usb-camera-app** streams video from a USB webcam connected to the Qualcomm EVK. This webcam should be accessible as a /dev/videoX device. Additionally, you can perform object detection and preview the results. - **[Hardware benchmarking application](https://docs.qualcomm.com/doc/80-70020-50/topic/vision_ai_demo_app.html)** The hardware benchmarking application monitors the device hardware usage for a defined set of sample applications to capture metrics such as CPU/GPU/NPU usage and device thermals. These metrics explain the resource usage and throttling, which help to tune your AI use cases according to the requirements. **Parent Topic:** [Sample applications](https://docs.qualcomm.com/doc/80-70020-50/topic/example-applications.html) Last Published: Jan 30, 2026 [Previous Topic Sample applications](https://docs.qualcomm.com/bundle/publicresource/80-70020-50/topics/example-applications.md) [Next Topic Prerequisites](https://docs.qualcomm.com/bundle/publicresource/80-70020-50/topics/download-model-and-label-files.md)