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Yes—YOLOv5 can run on the Mixtile Blade 3 through Rockchip’s RKNN model and runtime path, which uses the RK3588’s NPU. The practical route is to convert or obtain an RKNN-ready model, install a runtime compatible with the board’s Ubuntu image, and run an RKNN demo or application. Do not assume NVIDIA CUDA or GPU-focused Docker instructions apply.
Can the Blade 3 run YOLOv5?
The Mixtile Blade 3 is an ARM64 single-board computer based on the Rockchip RK3588. Its NPU makes it suitable for edge inference, but a standard YOLOv5 model is not automatically an NPU-ready deployment. The usual approach is to convert the model to Rockchip’s RKNN format, or start with a compatible RKNN model, then run inference with the matching Rockchip runtime.
Check Mixtile’s manual for the exact Blade 3 revision in use. RAM, eMMC, connectors, and carrier details can differ by revision, so do not assume one board’s configuration describes every Blade 3.
What do you need before setting it up?
- A supported Ubuntu image for the Blade 3 and a working Rockchip NPU driver/runtime.
- YOLOv5 weights and either a host machine for conversion or an RKNN-ready model.
- A board-side RKNN Toolkit2 Lite/runtime package that matches the software stack.
- A compatible ARM64 Python environment or container, plus any libraries required by the chosen demo.
- An image, camera, or video source for inference. Camera, storage, power, and cooling compatibility depend on the specific board and accessories.
The RK3588 project’s documented WebUI route uses Ubuntu, FFmpeg and related libraries, ARM64 Miniconda, Python 3.9, rknn_toolkit_lite2, and the repository’s requirements. It also documents an optional Docker route. Treat those as that project’s setup rather than a guarantee that every Blade 3 image or runtime will use identical versions.
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How to deploy YOLOv5 through RKNN
- Prepare the board. Install a supported Ubuntu image for the Blade 3, then confirm the Rockchip NPU driver and runtime are present and functional.
- Choose the model path. Obtain YOLOv5 weights and convert them to RKNN on a host machine if required, or use an RKNN-ready model intended for the RK3588.
- Match the software versions. Set up an ARM64 Python environment or suitable container and install the RKNN Toolkit2 Lite/runtime package compatible with the board’s driver and OS image. Install the selected demo’s Python dependencies as well.
- Run an inference example. Launch a YOLOv5 RKNN demo or WebUI with an image, camera, or video input. Confirm that the application is using the RKNN path rather than an unsupported CUDA configuration.
- Measure the whole pipeline. Record whether preprocessing, postprocessing, display, and recording are enabled; those operations affect end-to-end throughput.
Ultralytics’ generic Docker quickstart includes commands such as python train.py, python val.py, python detect.py, and python export.py. Its GPU setup assumes NVIDIA drivers and NVIDIA Container Toolkit, which is a different deployment path from the RK3588 NPU workflow. Use Rockchip-specific RKNN instructions for Blade 3.
What FPS can you expect on an RK3588?
Published figures illustrate how widely throughput can vary with model and test setup; they are not a guaranteed Blade 3 result. Qengineering’s 2024 RK3588 table reports the following model-specific figures. That table says models are INT8-quantized unless noted, and uses separate model and input conditions, so the rows should not be treated as a controlled comparison under one shared configuration.
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| YOLOv5 variant | Reported throughput | Source and qualification |
|---|---|---|
| yolov5n | 58.8 FPS | Qengineering, 2024; model/input conditions differ across entries, and models are INT8 unless noted. |
| yolov5s_relu | 50.0 FPS | Qengineering, 2024; model/input conditions differ across entries, and models are INT8 unless noted. |
| yolov5s | 37.7 FPS | Qengineering, 2024; model/input conditions differ across entries, and models are INT8 unless noted. |
| yolov5m | 16.2 FPS | Qengineering, 2024; model/input conditions differ across entries, and models are INT8 unless noted. |
A separate Applied-Deep-Learning-Lab RK3588 project reports that recording reduced its frame rate by about 20 FPS and that it could reach around 60 FPS without recording. That is a project-specific report, not a controlled Blade 3 benchmark or a promise of sustained performance on every board.
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FPS numbers are useful only when the test conditions are clear. Record these details with any result:
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- YOLOv5 variant and whether the model is quantized.
- Input resolution and RKNN runtime version.
- Preprocessing and postprocessing included in the timing.
- Whether input comes from a camera or file, and whether display or recording is active.
- Ubuntu image, power and thermal conditions, and the Blade 3 revision.
For a practical application, measure with the same recording and display behavior you intend to use in deployment. An inference-only figure can overstate the throughput users experience when the full video pipeline is active.
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