Hi @Alex-Kushleyev,

Here are links to the relevant parts of my source code:
https://pastebin.com/aXYrE9qZ
https://pastebin.com/CkHsVC5N
Additionally, I have attached another image below showing the version of the VOXL2 SDK I am using for your reference

Karl

Hi @Alex-Kushleyev,

Thank you for your response. Attached below is a screenshot of my subscription code for your reference:


I suspect the issue might be related to the pipe_client_get_next_available_channel() function. Please let me know if there are any specific tests you would recommend to further diagnose this issue.

Karl

Hi @Zachary-Lowell-0. We have confirmed that the camera is functioning correctly, so it seems unlikely that the issue is camera-related. Since the problem occurs at the output_tensor float pointer rather than a frame nullptr, the root cause appears to be elsewhere. Additionally, upon further inspection, I noticed that the label_in_use parameter no longer exists starting from v0.3.5. Could you clarify where the label file is defined in versions after v0.3.5? For now, we are using v0.3.4, which works as expected.

Picture above is a debugger mode result showing the output_tensor state, where data is present in bbox_list, confirming that the frame is working correctly. I am attempting to access data[index+4] in the file inference_helper.cpp on line 770, as shown in the picture below.

I've been working with the ModalAI server and encountered a warning while creating a client that subscribes to the server. The warning states: "too many clients are trying to connect," even when the machine has just been rebooted.

Upon inspecting the source code, the warning occurs when no channels are available (i.e., no channel is uninitialized or disconnected). This behavior seems abnormal during a reboot.

Has anyone experienced similar issues or have insights into why channels might be occupied right after a reboot? Any suggestions for further debugging or resolving this would be appreciated.

I've been working with tflite server and encountered an issue when trying to run inference with my models.

When using version v0.3.1, the server works correctly with a normal model. However, when switching to a custom model with only two labels, the server shuts down due to a segmentation fault.

Upon testing with version v0.3.5, the server shuts down regardless of whether I use a custom model or a regular model. I've tried enabling debug mode, and it reveals that the issue occurs in the InferenceHelper::postprocess_yolov5 function. Specifically, the error arises in the getbbox function where it attempts to access an invalid memory location in the output_tensor float pointer.

Has anyone else encountered this issue or have suggestions for debugging this further? Is it a bug in the latest version, or could it be related to how the models are exported? Any guidance would be appreciated.