Coral dev board and YOLO for object detection.

[MECHAAMER]

Description

I'm working on training a Yolo model for object detection and plan to use a Google Coral Dev Board for inference. As the Coral documentation recommends, the model should be in the TFLite format with 8-bit quantization for optimal performance.

Thanks to Ultralytics, exporting the model to the required format is straightforward: Python

from ultralytics import YOLO 

model = YOLO("pre_trained_model.pt")

# Export the model to TFLite Edge TPU format 
model.export(format="edgetpu")

In the output, I see: Number of operations that will run on Edge TPU: 425 Number of operations that will run on CPU: 24

My question is: Can I do anything to make all operations run on the TPU for faster processing?

Additionally, are there any other recommended models that might offer better accuracy and lower latency on a Google Coral board?

Thank you all.

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Issue Type

Performance, Support

Operating System

Ubuntu

Coral Device

Dev Board

Other Devices

No response

Programming Language

Python 3.7, Other

Relevant Log Output

No response