Machine Learning Guide¶
Our newest QSXP-ML81 CoM with the NXP i.MX8M Plus processor comes with a Machine-Learning-Unit (MLU) on-board.
There are different Machine Learning demos you can run on the processor’s NPU.
Further information can be read at the i.MX Machine Learning User’s Guide
|QSXP-ML81||qsxp-ml81-ml in Download Area||Machine Learning Demo Image.|
|TX8P-ML81||tx8p-ml81-ml in Download Area||Machine Learning Demo Image.|
|QSXP-ML81||qsxp-ml81-basler in Download Area||Machine Learning Demo Image with Basler Camera support.|
For creating the neccessary RootFS, a complete Yocto build environment is required.
Depending on your module choose the correct guide - if not already set up.
QSXP, TX8P use NXP Yocto BSP Guide
- Setup the Yocto build-directory as described in the Yocto guide for your machine. Use the following values:
- Compile the image.
If you want to compile the image with basler camera support, follow the additional steps described at Basler Camera.
Live Object Detection¶
For this demo a camera is mandatory. The following example was tested with karo-image-basler and a Basler camera.
The pre-trained eIQ DeepViewRT model from NXP allows it, to run a live eIQ DeepViewRT GStreamer Detection Demo on the NPU of the i.MX8M Plus.
- Download the
eIQ Demo File Archiveand unpack it to your target.
- On the target, run the following command to show the example using the CPU:
# ./ssdcam-gst -m mobilenet_ssd_v1_1.00_trimmed_quant_anchors.rtm -c /dev/video2
- Run the following commands to show the example using the NPU:
# modelrunner -m mobilenet_ssd_v1_1.00_trimmed_quant_anchors.rtm -e ovx -H 10818 & # ./ssdcam-gst -m mobilenet_ssd_v1_1.00_trimmed_quant_anchors.rtm -r 127.0.0.1 -u 1 -c /dev/video2