integrations/edge-tpu/

[giscus[bot]]

integrations/edge-tpu/

Discover how to uplift your Ultralytics YOLOv8 model's overall performance with the TFLite Edge TPU export format, which is perfect for mobile and embedded devices.

https://docs.ultralytics.com/integrations/edge-tpu/

[FearL0rd]

Hello my friends from Earth.
This is the compiler for ARM
https://github.com/FearL0rd/edgetpu_compiler_for_ARM64

[pderrenger]

Hello! Thank you for sharing the compiler link for ARM. This should be helpful for optimizing and compiling models specifically on ARM64 architectures.

For integrating the TensorFlow Lite Edge TPU export with your YOLOv8 models, make sure your models are fully quantized to leverage the Edge TPU's capabilities for accelerated inference on edge devices. Here’s a brief example of how you can export a YOLOv8 model to TFLite Edge TPU format:

# Export a YOLOv8n PyTorch model to TFLite Edge TPU format
yolo export model=yolov8n.pt format=edgetpu

# After exporting, you can run inference with the TFLite model
yolo predict model=yolov8n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'

If you have any more questions or need further assistance, feel free to ask. Happy modeling! 🚀

[FearL0rd]

How do I use YOLO with a Dual TPU card?

I have APEX_0 and APEX_1 devices.

https://coral.ai/products/m2-accelerator-dual-edgetpu/

[FearL0rd]

Streamline segmentation during export https://coral.ai/docs/edgetpu/pipeline/

[glenn-jocher]

@FearL0rd thank you for your suggestion! Streamlining segmentation during export is indeed a valuable enhancement. For detailed guidance on exporting YOLOv8 models to TFLite Edge TPU format, you can refer to our documentation. This will help you optimize your models for high-speed, low-power inferencing on devices with limited computational power.

For more information on using YOLO with a Dual TPU card, you can leverage the APEX_0 and APEX_1 devices to distribute the workload effectively. This setup can significantly boost performance and efficiency in your machine learning tasks.

Feel free to explore the provided resources and let us know if you have any further questions! 🚀

[FearL0rd]

@glenn-jocher where is the doc talking about segmentation using APEX_0 and APEX_1

[glenn-jocher]

Hi @FearL0rd,

For documentation on segmentation using APEX_0 and APEX_1, you can refer to the guide on exporting YOLOv8 models to TFLite Edge TPU format. This guide covers optimizing your models for high-speed, low-power inferencing on devices like the Coral Edge TPU.

To utilize both APEX_0 and APEX_1 devices, you can distribute the workload effectively across the dual TPU setup, enhancing performance and efficiency. For more detailed instructions, please visit the relevant sections in our documentation.

Feel free to reach out if you have any more questions! 😊