# Run AI sample applications

AI sample applications implement Qualcomm Neural Processing SDK (QNN) based image detection in typical scenarios.

- Detect hands with `sample_hand_detection`
The `sample_hand_detection` sample application uses Python-based hand recognition ROS node to detect hand and hand movement. It uses [Qualcomm AI Engine Direct SDK (QNN)](https://www.qualcomm.com/developer/software/qualcomm-ai-engine-direct-sdk) for model inference.
- Classify images with `sample_resnet101`
`sample_resnet101` uses Python to performs image classification, in which process it uses Qualcomm® AI Engine Direct SDK (QNN) for model inference.
- Estimate image depth values with `sample_depth_estimation`
The `sample_depth_estimation` sample application accepts an RGB image named `input_image.jpg` or subscribes to the `/cam0_stream1` topic from the `qrb_ros_camera` node as the input. It uses Qualcomm® AI Engine Direct SDK (QNN) for inference and publishes the results to the `/depth_map` topic with per-pixel depth values.
- Detect faces with `sample_face_detection`
The face detection sample application detects face and locates facial features from the face image using the Python-based ROS node `sample_face_detection`, which uses Qualcomm® AI Engine Direct SDK (QNN) for model inference.
- Estimate human poses with `sample_hrnet_pose_estimation`
This pose estimation sample application uses the `sample_hrnet_pose_estimation` ROS node to provide high-precision human pose estimation capabilities.

Last Published: Nov 05, 2025

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