am trying to deploy a classifier model that takes the whole frame and classify the frame if it’s “a class” or “not that class”,
so the input is a frame then classify that frame and get the classification output only ,
I am converting my model to onnx then to .engine format by using trtexc,
then i use the pgie config of the deepstream test1 and put my engine and onnx file at that config, also i edit the label file and the code inside deepstream_test1.py to put my labels such as “violence and not violence”,
i dnt know what to do next !, i read there’s other steps for creating a custom pareser and plugins and i dnt have a complete idea about the remaining steps, i feel confused if what i have tried is right or missing something, and also confused about the next step, hope someone will help
More details about the model layers:
Input Tensor:
(“input_1:0”, shape=(?, 224, 224, 3), dtype=float32)
it consists of Conv2d followed by Maxpooling
Output Tensor:
(“dense_2/Softmax:0”, shape=(?, 2), dtype=float32)
2 fully connected layers with softmax
**• Hardware Platform: GPU
**• DeepStream 5
• TensorRT Version= 7
**• NVIDIA GPU Driver Version: 450.66 **
• Issue Type is questions)