Is it able to run YOLOv5 inference on non-NVIDIA GPUs?

[khanhtrannnn]

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Question

I'm working with the Khadas VIM3 board, a single-board computer. It uses a Mali-G52 MP4 GPU, which is not an NVIDIA GPU. It appears that YOLOv5 is compatible only with CUDA devices, as the debug log indicates a requirement for either CPU-only or CUDA devices.

python3 detect.py --weights yolov5s-fp16.tflite --img-size 384 640 --conf 0.01 --line-thickness=1 --source data/videos/traffic5s.mp4 --device 0

AssertionError: Invalid CUDA '--device 0' requested, use '--device cpu' or pass valid CUDA device(s)

Could someone please confirm if YOLOv5 can utilize non-NVIDIA GPU? Thank you.

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[github-actions]

👋 Hello @khanhtrannnn, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@khanhtrannnn hello! YOLOv5 primarily supports NVIDIA GPUs through CUDA for acceleration. However, you can run YOLOv5 on CPU or use the ONNX/TFLite models for running on other platforms that may support those formats. For a Mali-G52 MP4 GPU, you might want to explore running the TFLite model with inference backends that support Mali GPUs, such as Arm NN or TFLite's GPU delegate. Keep in mind that performance may vary and you might need to handle additional integration steps. For more detailed guidance, please refer to our documentation. Happy coding! 😊🚀

[github-actions]

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