Please provide complete information as applicable to your setup. • Hardware Platform (Jetson / GPU) Jetson • DeepStream Version 6.2 • JetPack Version (valid for Jetson only) 5.1 • TensorRT Version 8.5.2 I’m trying to implement the below pipeline in deep stream Face detection → Tracking → Recog…

The related documents: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvinfer.html#tensor-metadata https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_metadata.html And the sample code deepstream_tao_apps/apps/tao_others/deepstream-mdx-perception-ap…

Seems your facenet postprocessing is wrong. 0:00:56.738614428 19297 0x31ed85e0 ERROR nvinfer gstnvinfer.cpp:674:gst_nvinfer_logger:<secondary-inference face_classifier> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::parseBoundingBox() <nvdsinfer_context_impl_output_parsi…

Does that mean that there’s ab error in converting the facenet model from Pytorch to ONNX? I have followed tensorrt official guide. Additionally where to config the facenet as recognition i have wrote over test-2 sample where 2nd configuration is the recognition part

[image] axnet: Does that mean that there’s ab error in converting the facenet model from Pytorch to ONNX? No. When you choose the model, please also understand the input and output of the model. There are thousands of models for different purpose, the TensorRT only do the ineferencing, you n…

In my case Facedetect gives two outputs and Facenet gives one output which is the 512 embeddings. I wrote over test-2 to add the classification which compares the generated embeddings with embeddings in a dataset. Can you please support what preprocessing/post processing technique should be used and…

For facedetect, there is already sample in deepstream_tao_apps/configs/nvinfer/facial_tao/config_infer_primary_facenet.txt at master · NVIDIA-AI-IOT/deepstream_tao_apps (github.com) For your facenet, we don’t know anything about your model. The only sample we can provide you is for how to get the t…

is there any documentation for the user_meta? I;m trying to understand more about it. I can run the pipeline without errors now but i think detected faces in PGIE are not passed to SGIE because these faces have to be resized to (160,160). How can i add add this pre-processing to the pipeline. I’m us…

which property in the config file will do the scaling part? I have tried to disable input/output tensort-meta to use the defult preprocess but obj_meta.obj_user_meta_list None always none. when enabling output tensort-meta only obj_meta.obj_user_meta_list passed one face out of all detected faces

ERROR: Could not find output coverage layer for parsing objects / Failed to parse bboxes

Accelerated Computing Intelligent Video Analytics DeepStream SDK

Fiona.Chen September 25, 2023, 3:01am 10

The SGIE preprocess is done inside gst-nvinfer. gst-nvinfer knows the model input dimensions, you just need to tell gst-nvinfer which scaling method do you want by the configuration file. DeepStream SDK FAQ - Intelligent Video Analytics / DeepStream SDK - NVIDIA Developer Forums

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ERROR: Could not find output coverage layer for parsing objects / Failed to parse bboxes

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