NVIDIA
NVIDIA
SE-ResNeXt101-32x4d Triton deployment for PyTorch
Resource
NVIDIA
NVIDIA
SE-ResNeXt101-32x4d Triton deployment for PyTorch

Deploying high-performance inference for SE-ResNeXt101-32x4d model using NVIDIA Triton Inference Server.

This resource is a subproject of se_resnext_for_pytorch. Visit the parent project to download the code and get more information about the setup.

The SE-ResNeXt101-32x4d is a ResNeXt101-32x4d model with added Squeeze-and-Excitation module introduced in Squeeze-and-Excitation Networks paper.

The SE-ResNeXt101-32x4d model can be deployed for inference on the NVIDIA Triton Inference Server using TorchScript, ONNX Runtime or TensorRT as an execution backend.

Publisher
NVIDIA
NVIDIA
UpdatedApril 4, 2023 UTC

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