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.
Click to expand!
### 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_
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
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.
Click to expand!
### 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_