# No detections when running custom YOLOv8 model on voxl-tflite-server Source: https://forum.modalai.com/topic/4908/no-detections-when-running-custom-yolov8-model-on-voxl-tflite-server Category: VOXL 2 and VOXL 2 Mini (https://forum.modalai.com/category/26/voxl-2-and-voxl-2-mini) Tags: voxl-2 Posted: 2025-12-05 22:13:53 UTC by svempati Replies: 16 · Views: 2216 ## svempati · 2025-12-05 22:13:53 UTC Hello, I am trying to run a custom YOLOv8 model on the voxl-tflite-server. The model detects ships and the `yolov8_labels.txt` file only contains one `ship` class. However, when I run the tflite server and view it on `voxl-portal` I can see the video feed, but cannot see any bounding box detections even when the target is in the camera frame. I tried another variation for the labels file by having the class index and label name like this: `0 ship`, but that doesn't work either. I also ran `voxl-inspect-detections` but it doesn't show any detections there. When I tested the default yolov5 and yolov8 models on `voxl-tflite-server`, it displays the bounding boxes and shows the list of detections in `voxl-inspect-detections` just fine. If it helps, I used this command to convert the YOLOv8 model to the tflite format: ``` yolo export model=best.pt format=tflite ``` I use the quantized 16 bit tflite model named `yolov8_best_float16.tflite`. This is how I set up the config file `/etc/modalai/voxl-tflite-server.conf`: ``` { "skip_n_frames": 0, "model": "/usr/bin/dnn/yolov8_best_float16.tflite", "input_pipe": "/run/mpa/front_small_color/", "delegate": "gpu", "requires_labels": true, "labels": "/usr/bin/dnn/yolov8_labels.txt", "allow_multiple": false, "output_pipe_prefix": "yolov8" } ``` Is there anything I missed that is leading to no detections on the `voxl-tflite-server`? I would appreciate any help! ## Reply by Zachary Lowell 0 · 2025-12-08 15:47:59 UTC Hello @svempati can you paste your yolov8_labels and tflite file and I can test it out on my end? To confirm - you followed the instructions in this gitlab repository: https://gitlab.com/voxl-public/support/voxl-train-yolov8 Zach ## Reply by svempati · 2025-12-08 16:54:47 UTC (in reply to Zachary Lowell 0) Hi @Zachary-Lowell-0, Yes I am confirming that I followed the instructions in that gitlab repository. Here is the tflite file and labels file: [https://drive.google.com/drive/folders/1kyjanabVSP_pH_jsQyjQG9z6hFYZ_iij?usp=drive_link](https://drive.google.com/drive/folders/1kyjanabVSP_pH_jsQyjQG9z6hFYZ_iij?usp=drive_link) ## Reply by Zachary Lowell 0 · 2025-12-08 17:24:19 UTC @svempati said in [No detections when running custom YOLOv8 model on voxl\-tflite\-server](/post/24559): > "labels": "/usr/bin/dnn/yolov8_labels.txt", > So running your model we get the following errors via voxl-tflite-server: ``` Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here ``` Which means there is an issue in your model itself and most likely means you ran into an issue during the build process. Specifically this means that is a model issue, not a labels file issue. Your .tflite model has an output tensor with a different data type than what voxl-tflite-server expects The code itself shows the error when you hit this case statement: // Gets the uint8_t tensor data pointer template <> inline uint8_t *TensorData(TfLiteTensor *tensor, int batch_index) { int nelems = 1; for (int i = 1; i < tensor->dims->size; i++) { nelems *= tensor->dims->data[i]; } switch (tensor->type) { case kTfLiteUInt8: return tensor->data.uint8 + nelems * batch_index; default: fprintf(stderr, "Error in %s: should not reach here\n", __FUNCTION__); } return nullptr; } Which means the output tensor doesnt match the expected output in this header file. Please look into your model. zach ## Reply by Zachary Lowell 0 · 2025-12-08 17:49:20 UTC https://gitlab.com/voxl-public/voxl-sdk/services/voxl-tflite-server/-/blob/master/include/tensor_data.h These are all the potential data types that voxl-tflite-server is expecting. ## Reply by svempati · 2025-12-08 17:58:33 UTC (in reply to Zachary Lowell 0) I see, so my model is not supported by the voxl-tflite-server since it is float16 and the tflite server only supports 32 bit precision if we want to use floating point values. Am I understanding that correctly or am I missing something? Cause the default YOLOv5 model that is included in the VOXL 2 model (`yolov5_float16_quant.tflite`) is also of float 16 precision so I wonder how the functions in `tensor_data.h` handle that. One question, what command did you use to view these error logs from voxl-tflite server? ``` Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here Error in TensorData: should not reach here ``` ## Reply by Zachary Lowell 0 · 2025-12-08 18:12:18 UTC @svempati said in [No detections when running custom YOLOv8 model on voxl\-tflite\-server](/post/24577): > 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 ## Reply by svempati · 2025-12-08 18:34:41 UTC (in reply to Zachary Lowell 0) @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! ## Reply by svempati · 2026-02-02 18:05:14 UTC (in reply to Zachary Lowell 0) @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: 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! ## Reply by Zachary Lowell 0 · 2026-02-03 15:23:07 UTC @svempati said in [No detections when running custom YOLOv8 model on voxl\-tflite\-server](/post/25115): > WARNING: Unknown model type provided! Defaulting post-process to object detection. > @svempati I will try recreating this issue today and get back to you! ## Reply by svempati · 2026-02-09 21:28:26 UTC (in reply to Zachary Lowell 0) @Zachary-Lowell-0 Just wanted to follow up to see if you were able to replicate this issue? ## Reply by Zachary Lowell 0 · 2026-02-11 19:44:33 UTC @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? ## Reply by svempati · 2026-02-12 00:47:47 UTC (in reply to Zachary Lowell 0) @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! ## Reply by Zachary Lowell 0 · 2026-02-17 16:29:08 UTC 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! ## Reply by Zachary Lowell 0 · 2026-02-17 22:52:44 UTC https://www.loom.com/share/bf52e252ab09444bb366f265a3f36dc5 Alright take a look at this loom please - it might help point you in the right direction in terms of training your model. Zach ## Reply by svempati · 2026-02-18 18:12:13 UTC (in reply to Zachary Lowell 0) @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](https://gitlab.com/voxl-public/support/voxl-train-yolov8/-/blob/master/Dockerfile?ref_type=heads) 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? ## Reply by ApoorvThapliyal · 2026-03-02 20:51:47 UTC (in reply to svempati) 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