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Deep learning project with TensorRT API

  • Posted at : 1 month ago
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  • Python
  • CNN
  • Machine
  • Learning
  • Vision

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SR

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Convert your PyTorch model to ONNX (or TensorFlow model to UFF)
2. Use TensorRT’s C++ API to parse your model to convert it to a CUDA engine. TensorRT engine would automatically optimize your model and perform steps
like fusing layers, converting the weights to FP16 (or INT8 if you prefer) and
optimize to run on Tensor Cores, and so on. . Perform inference and print two things:
 a. Accuracy

b. Latency