Deploying YOLO on the RK3588 is not just about getting a demo to run—it's about making the model run stably and efficiently on the board. Many teams can help you set up the environment and load the weights, but what truly determines the success of your project is often the details: frame rate, memory usage, and multi-channel video processing. So when choosing a team, don't just look at whether they "know AI" or "can code." Instead, focus on whether they have hands-on experience with embedded AI deployment, especially familiarity with the Rockchip platform.
The ideal team would be one that understands both deep learning model optimization and embedded systems development. Such a team can perform pruning, quantization, and operator replacement on the YOLO model specifically for the RK3588's NPU features, rather than simply converting a PyTorch model to ONNX and forcing it to run. They know which layers can run on the NPU and which must fall back to the CPU, and they can adjust input resolution or inference batch size based on your actual scenario to strike a balance between accuracy and speed. In short, what they deliver is not just "it runs," but "it runs smoothly on the RK3588."
You should also check whether the team is familiar with the RK3588 development environment—such as the version pitfalls of RKNN-Toolkit, the cross-compilation toolchain, multi-core scheduling, and memory bandwidth limitations. Teams without real-world experience in these areas are unlikely to avoid these issues in advance, and problems often surface only during integration testing, causing project delays.
If your scenario involves camera input, RTSP streaming, or multi-channel concurrent processing, the team also needs video pipeline or streaming media development capabilities. YOLO is just one piece of the puzzle—decoding, encoding, and transmission on both ends also affect the overall performance. Such teams typically have experience with complete software-hardware solutions and can take you from board selection and thermal design to algorithm deployment in one seamless flow, rather than just handing over a few code files.
Finally, try to avoid teams with a purely academic background or those that only know how to do server-side deployment. The RK3588 has limited resources, and how you run models on it is completely different from on an x86 server. A reliable team will first ask about your frame rate requirements, number of detection targets, and operating environment before proposing a solution—they won't simply say "no problem" without asking anything. If a team has prior deployment cases on RK platforms, even with different algorithms, they deserve priority consideration—at least it shows they've hit the pitfalls of these boards and know how to make models work in real-world scenarios.