Classifier result on onnx doesn't match Deepstream result


Thank you both for the clarification.
It seems the root cause is from TensorRT rather than Deepstream.

Could you share the sample that can output expected (onnxruntime?) and incorrect (TensorRT) results with us?
We want to check this with the TensorRT team.


I have managed to add some code to dump out our result data of running efficientdet-d0.onnx by OnnxRuntime to the json file onnxruntime_result.json with the same format as that of trtexec-result.json, which was output by running trtexec with efficientdet-d0.onnx.
And so, the raw data, which is preprocessed (BGR2RGB, scaling,padding,normalization …) for efficientdet-d0.onnx with OnnxRuntime, is also written into a file o4_clip-1_raw_data.bin, I got trtexec-result.json by runing trtexec with --loadInputs=‘data’:‘o4_clip-1_raw_data.bin’, you can also do test with it for investigation.
The final results with bbox(s) drawn on original image are also attached for your reference:
o4_clip-1-OnnxRuntime_result.jpg is the final result parsed from the output of running OnnxRuntime with efficientdet-d0.onnx, and o4_clip-1-TensorRT_result.jpg is the final result parsed from trtexec-result.json, which is the output .json file of running trtexec with efficientdet-d0.onnx. All bboxes are filtered with score_threshold=0.1 and iou_threshold=0.1.
Please note the above efficientdet-d0.onnx was not exported out with the standard efficientdet-d0.pth weight file(which was trained out with coco dataset), it was exported out with a weight file which was trained out with our own dataset, there is only one class: baggage. The original image file o4_clip-1.png is also attached for your reference.

Thanks! (3.6 MB) (2.1 MB) (3.3 MB)

Sorry, for the onnx, please use this (2.0 MB)

Hello, any update ? thanks.

Please ignore the result seen in o4_clip-1-TensorRT_result.jpg attached last time, I forgot to scale bbox back to their actual size with the scale ratio used in image preprocessing for network, so the size and position of the two bboxes in o4_clip-1-TensorRT_result.jpg are not right.
Now I have corrected this error and did more testing and collected the image raw data and the results got by OnnxRutime vs trtexec, you can see, sometime onnx running with trtexec could recognize the same targets, but the score is much smaller than that got by onnx running with OnnxRuntime, and sometime the score for a target was under 0.1 (I set the confidence threshold to 0.1), so no bbox was drawn out (Please see o4_clip-5-TensorRT_result.jpg vs o4_clip-5-OnnxRuntime_result.jpg).

Please use RAR tools to extract the attached zip files as a whole: (4 MB) (4 MB) (4 MB) (3.8 MB)


Thanks for sharing the information.

There is a similar issue in Inference of model using tensorflow/onnxruntime and TensorRT gives different result and we are checking it right now.

Will update here for any further progress.


We have confirmed that the above topic is a user bug in pre-processing.
Could you check if you are meeting the similar first?


No, I think the cause of the precision degradation I saw is totally different, as I got the raw input data for OnnxRuntime’s run() by np.tofile(), the same raw data runs with OnnxRuntime’s run() could get much better result than that runs with trtexec. You can have a test with the raw data which I uploaded in the zip file on 5/Nov, to compare trtexec and OnnxRuntime.

I have a similar issue with efficentnet and efficientdet when converting from onnx to tensorrt. In onnxruntime the predictions are fine but when doing inference with tensorrt I only get a zero tensor

Hi, both

Thanks for your reporting.

We are trying to reproducing this issue with the date shared by akachen in Nov 5.
Will let you know for any progress.


Hi, akachen

Sorry for the late.
We try to reproduce this issue with following file: (4 MB) (4 MB) (4 MB) (3.8 MB)

But found that we cannot extract the onnx file correctly.
Could you re-compressed it and share it with us again?


Sorry, I forgot to attach the left parts of, I attach all of them here: (2.0 MB) (4 MB) (4 MB) (4 MB)

After download all the above 4 files to a same directory, change the name of ''" to “efficientdet-d0-s.z01”, and do the same changes to “” and “”, then unzip to get efficientdet-d0-s.onnx

Thanks, we are able to download the model file.
Will share more information with you later.


Thanks for your sample and data.
We can reproduce this problem internally and pass it to our internal team for checking.

We will share more information here once we found anything.


Hello, is this issue resolved or not ? thanks.

Not yet.


Sorry that we are still checking this issue internally.
Will update more information with you later.


I have some updates regarding this issue. I managed to deserialize the engine that generated from DeepStream in Tensorrt and I make sure that trt_sample.cpp (8.5 KB) the result is correct and same as the actual model, I am sure now the issue is coming from deepstream preprocessing. I reproduced the issue to understand what is the actual preprocessing in deepstream. I deleted all preprocessing steps in deepstream also tensorrt and I am trying to get the same result.,
attached tenserrt code and config file on deepstream.

The pre-processing function that I read in nvidia document is:
where is the code in deepstream for preprocessing so I can updated?

config_seat_belt_violation.txt (721 Bytes)


Thanks for sharing this information.
You can find the pre-processing in the nvdsinfer component:


NvDsInferStatus InferPreprocessor::transform(
    NvDsInferContextBatchInput& batchInput, void* devBuf,
    CudaStream& mainStream, CudaEvent* waitingEvent)


it seems that the difference because the way that Deepstream read the images. when we compare the pixels value in deepstream before preprocessing it is not same as how opencv read the image, why there is a difference? and how can we get the same exact confidence value of the actual model