Radxa Zero 3 NPU with Ubuntu 22.04

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Ubuntu 24.04

Better to use our latest version Ubuntu 24.04.

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Installation.

• Get a 16 GB (minimal) SD card holding the image.
• Download the Radxa_Zero3_NPU_Ubuntu22.img.xz image (2.9 GByte) from our Sync site.
• Flash the image on the SD card with the Imager or balenaEtcher.
• Insert the SD card in your Rock 5 and enjoy.
• Username: radxa
• no password: radxa

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NPU.

• To get the NPU working, see How to Enable the NPU on the Radxa Zero 3.

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Model performance benchmark(FPS)

All models, with C++ examples, can be found on the SD image.<br>

| demo | model_name | inputs_shape | dtype | Radxa Zero3| | ---------------- | ---------------------------- | ----------------------- | ----- | ------------- | | yolov5 | yolov5s_relu | [1, 3, 640, 640] | INT8 | 14.8 | | | yolov5n | [1, 3, 640, 640] | INT8 | 19.5 | | | yolov5s | [1, 3, 640, 640] | INT8 | 11.7 | | | yolov5m | [1, 3, 640, 640] | INT8 | 5.7 | | yolov6 | yolov6n | [1, 3, 640, 640] | INT8 | 18.0 | | | yolov6s | [1, 3, 640, 640] | INT8 | 8.1 | | | yolov6m | [1, 3, 640, 640] | INT8 | 4.5 | | yolov7 | yolov7-tiny | [1, 3, 640, 640] | INT8 | 16.1 | | | yolov7 | [1, 3, 640, 640] | INT8 | 3.4 | | yolov8 | yolov8n | [1, 3, 640, 640] | INT8 | 18.2 | | | yolov8s | [1, 3, 640, 640] | INT8 | 8.9 | | | yolov8m | [1, 3, 640, 640] | INT8 | 4.4 | | yolox | yolox_s | [1, 3, 640, 640] | INT8 | 10.0 | | | yolox_m | [1, 3, 640, 640] | INT8 | 4.8 | | ppyoloe | ppyoloe_s | [1, 3, 640, 640] | INT8 | 9.2 | | | ppyoloe_m | [1, 3, 640, 640] | INT8 | 5.0 | | yolov5_seg | yolov5n-seg | [1, 3, 640, 640] | INT8 | 1.04 | | | yolov5s-seg | [1, 3, 640, 640] | INT8 | 0.87 | | | yolov5m-seg | [1, 3, 640, 640] | INT8 | 0.71 | | yolov8_seg | yolov8n-seg | [1, 3, 640, 640] | INT8 | 0.91 | | | yolov8s-seg | [1, 3, 640, 640] | INT8 | 0.87 | | | yolov8m-seg | [1, 3, 640, 640] | INT8 | 0.7 | | RetinaFace | RetinaFace_mobile320 | [1, 3, 320, 320] | INT8 | 88.5 | | | RetinaFace_resnet50_320 | [1, 3, 320, 320] | INT8 | 11.8 | | PPOCR-Det | ppocrv4_det | [1, 3, 480, 480] | INT8 | 15.1 | | PPOCR-Rec | ppocrv4_rec | [1, 3, 48, 320] | FP16 | 17.3 |

• Due to the pixel-wise filling and drawing, segmentation models are relatively slow

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Cooling.

You must cool your Zero3. It will get very hot without a heatsink.<br> We used a heatsink with two fans designed for the Raspberry Pi Zero, and it works fine.<br> Even with the NPU running 24/7 at 1.8 GHz, it never gets warmer than 42°C (107°F).<br>

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Use the thermal pad properly.<br> It should fill the space between the chip and the cooling element effectively.<br> If there is any gap, the heat flow will not be optimal, resulting in a much hotter CPU.<br> The delivered pad will come with two plastic protective sheets. These must be removed before applying the pad.<br> If there is still a small gap (the CPU of the Radxa is slightly thinner than the Raspberry Pi), cut some small slices from the pad and stack them.<br> The actual CPU core is located at the centre of the chip, where the heat is generated.<br><br>

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Tips.

• If you need extra space delete the opencv and the opencv_contrib folder from the SD card. They are no longer needed since all libraries are stored in the /usr/ directory.
• Use a tool like GParted sudo apt-get install gparted to expand the image to larger SD cards. We recommend a minimum of 64 GB. Deep learning requires a lot of space.<br/>

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Pre-installed frameworks.

• OpenCV 4.10.0
• NPU rknpu2 1.5.2
• NPU model zoo 2.0.0
• NPU model zoo models 2.0.0

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Thanks.

A more than special thanks to Joshua Riek for all the hard work on the Ubuntu OS.

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