1，What does output-blob-names mean? The official documentation on output-blob-names only says one sentence “output layer name”, which is really not very clear. Can you help me to explain, I don’t particularly understand, is it used to define neural network layers in the configuration? Or?
2，What should I understand about the difference between output-blob-names and parse-bbox-func-names?
Yes. “output-blob-names” is to fill the model network output layers’ names. It is not to define but to tell nvinfer some basic information of the model.
“parse-bbox-func-names” is the callback postprocessing function name for detector. It is only for customized postprocessing. Please refer to the nvinfer source code for details.
/opt/nvidia/deepstream/deepstream/sources/gst-plugins/gst-nvinfer and /opt/nvidia/deepstream/deepstream/sources/libs/nvdsinfer
Please read the code. It will be more clear and much better than raising topic in the forum.
1，Is the network layer set by “output-blob-names” already pre-defined in deepstream?
2，Where should I go to see all the pre-defined network layers in deepstream？so I can replace them and test them, in order to find a layer more suitable for my business scenario, after all the layers given in the case are very limited.
3，For example: output-blob-names=MarkOutput_0, where do I see the definition of MarkOutput_0?
DeepStream never trains any model. If you want the model training tool, please refer to TAO toolkit. TAO Toolkit | NVIDIA Developer
DeepStream can also adapt to caffe, ONNX, … These models are trained by Caffe https://caffe.berkeleyvision.org/, PyTorch https://pytorch.org/,…
Please ask the guy who trains and provides the model to you. If it is TAO pre-trained models, you can consult in TAO forum. Latest Intelligent Video Analytics/TAO Toolkit topics - NVIDIA Developer Forums
Please check with the guy who trains and provides the model to you.
1，Does the model of the network layer output defined by “output-blob-names” only support the caffe framework?
2，If I want to use pytorch, can’t I define it by “output-blob-names”, but only by customizing “engine-create-func-name”? to achieve this?
It works with caffe modle, uff model,…
How much do you know about TensorRT? TensorRT SDK | NVIDIA Developer
“output-blob-names” is not to define the output layers. The model is constructed and trained outside DeepStream, you need to fill “output-blob-names” to tell DeepStream(TensorRT) the layer names of the model. DeepStream never defines anything of the model.
“engine-create-func-name” is “Name of the custom TensorRT CudaEngine creation function”. Please refer to Using a Custom Model with DeepStream — DeepStream 6.1.1 Release documentation
Please read the user manual from the beginning. Welcome to the DeepStream Documentation — DeepStream 6.1.1 Release documentation
When I use yolov5s in deepstream, I don’t fill in “output-blob-names” to tell DeepStream (TensorRT) the layer name of the model. Instead, I directly configured the function using the post-processing parameter “parse-bbox-func-name”, why don’t custom ones like yolov5s need to configure “output-blob-names”?
Is your yolov5s model a ONNX model?
In yolov5s, I have converted to wts and cfg files that are entered through this configuration file.
custom-network-config=yolov5s_fast.cfg
model-file=yolov5s_fast.wts
If it is a caffe model，once the engine is generated through deepstream, I can get the data directly with the name of the output layer by output-blob-names=conv2d_bbox;conv2d_cov/Sigmoid?
There is no update from you for a period, assuming this is not an issue anymore.
Hence we are closing this topic. If need further support, please open a new one.
Thanks
For ONNX model or other pytorch models, “output-blob-names” is not necessary. For Uff and Caffe models, the wrong “output-blob-names” will cause the failure of building model engine. You will not get anything if “output-blob-names” is not filled correctly with Uff or Caffe models. Please ask the guy who provides the model to you for the output layer names.
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