No detections when running custom YOLOv8 model on voxl-tflite-server

[Zachary Lowell 0]

@svempati said in No detections when running custom YOLOv8 model on voxl-tflite-server:

I just ran voxl-tflite-server directly from the command line instead of in the background via systemd - aka run voxl-tflite-server directly on the command line. I would recommend NOT quantizing your model as the directions in the train yolov8 do not recommend that.

Zach

[svempati]

@Zachary-Lowell-0 Got it, I will try that out and will let you know if I have any more questions. Thanks for your help!

[svempati]

@Zachary-Lowell-0 I wanted to follow up with you again on this, and the issue seems to be the model conversion process from pytorch to tflite. To confirm this, I tried it with the default yolov8n.pt downloaded from ultralytics by entering this command from the gitlab repository:

python export.py yolov8n.pt

So that I create a new yolov8n_float16.tflite file. However, running this file on voxl-tflite-server shows this output before displaying Error in TensorData<float>: should not reach here:

WARNING: Unknown model type provided! Defaulting post-process to object detection.
INFO: Created TensorFlow Lite delegate for GPU.
INFO: Initialized OpenCL-based API.
INFO: Created 1 GPU delegate kernels.
Successfully built interpreter

------VOXL TFLite Server------

 4 5 6
 4 5 6
Connected to camera server

I even tried running export.py on the voxl emulator to account for any differences in the cpu architecture between my computer and the VOXL (between X86 and ARM) but I still get the same error. Do you think there is anything I would be missing? Thank you!

[Zachary Lowell 0]

@svempati said in No detections when running custom YOLOv8 model on voxl-tflite-server:

WARNING: Unknown model type provided! Defaulting post-process to object detection.

@svempati I will try recreating this issue today and get back to you!

[svempati]

@Zachary-Lowell-0 Just wanted to follow up to see if you were able to replicate this issue?

[Zachary Lowell 0]

@svempati I was able to use an open source training set and then leverage the docs to make my own custom yolov8 model capable of running on the voxl2 - do you want to provide to me the actual dataset and I can try creating the model and training it on a TPU?

[svempati]

@Zachary-Lowell-0 I would first like to diagnose what is causing the yolov8 model to not work on the voxl 2 for me. Will it only work when you train the model/ export it to tflite on a TPU? I am getting the issue even if I train the yolov8 model on an open source dataset/ use the pretrained yolov8n.pt model downloaded from ultralytics. I want to make sure I can train a yolov8 model on an open source dataset from scratch works on the voxl, so that I can move on to using my custom dataset.

In case there is no other workaround then I should be able to send you the dataset I am using.

Thanks!

[Zachary Lowell 0]

Let me try and train a custom model and run a loom on it in the next few days and get that over to you showing how I do it!

[Zachary Lowell 0]

ModalAI VOXL Portal - 17 February 2026

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Alright take a look at this loom please - it might help point you in the right direction in terms of training your model.

Zach

[svempati]

@Zachary-Lowell-0 Thanks for sharing the video! I looked at it, and I pretty much did the same steps you did. It might be worth mentioning that I had to modify the Dockerfile because the one in the documentation was throwing a version mismatch error when installing the onnx part.

This was the original docker command

RUN pip3 install ultralytics tensorflow onnx "onnx2tf>1.17.5,<=1.22.3" tflite_support onnxruntime onnxslim "onnx_graphsurgeon>=0.3.26" "sng4onnx>=1.0.1" tf_keras

I modified it to this

RUN pip3 install ultralytics tensorflow "onnx2tf>1.17.5,<=1.22.3" tflite_support onnxruntime onnxslim "onnx_graphsurgeon>=0.3.26" "sng4onnx>=1.0.1" tf_keras
RUN pip3 install onnx==1.20.1

I don't think this should cause any issues, but could you confirm?

[ApoorvThapliyal]

Hey @svempati
I trained a model in the docker command as provided by you. I am able to run an inference on potholes following exactly the steps provided by @Zachary-Lowell-0 earlier.
Could I ask you what your voxl-tflite-version is? And if you havent yet, could you try to run this version: http://voxl-packages.modalai.com/dists/qrb5165/sdk-1.6/binary-arm64/voxl-tflite-server_0.5.1_arm64.deb
Make sure to change the .tflite model and labels as shown in the loom video after the install, then run voxl-configure-tflite

Thanks