@atomsmasher9 said in Ultralytics version for voxl-tflite-server yolov8:

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Hi @atomsmasher9 - so ultralytics is leveraged for training - have you followed the instructions within this repository?

https://gitlab.com/voxl-public/support/voxl-train-yolov8/-/tree/master?ref_type=heads

Currently I have some updated written for this as well - so for example in export.py, this line: model.export("tflite") --> model.export(model = "tflite"), but I have yet to make the PR or commit for it - the README is relatively straight forward for how you can build a model leveraging docker, an nvidia gpu, and ultralytics to create the custom model. I was able to do these below and have a successful model created and uploaded to the voxl2 - ensure you place the model in /usr/bin/dnn directory alongside the labels.txt file to ensure you have the right labels - also you can manually update the file path in the conf file in /etc/modalai/voxl-tflite-server.conf.